Classifying AI-Empowered Software for Nonprofit Fundraising 

The expansive library of software options makes it an exciting and difficult choice to choose the most feasible system for an organization’s needs and budget. Upon beginning my research on AI in fundraising software, I was surprised at the distinctive and unique ways that each organization manipulates AI technology. Kanter and Allison Fine categorize these services in AI4Giving: Unlocking Generosity with Artificial Intelligence: The Future of Giving, a select few listed below:

• Donor Prediction Models and Automated StewardshipWorkflow: Machine learning core methods to train algorithms on donor data to identify the most likely donor prospects.
• Online Fundraising Campaigns: Machine learning analytics to analyze donor databases, sometimes appended to third-party data sets or social media data.

• Donor Research/Data Collaboratives: Platforms that are sharing data for research purposes with privacy protocols and ethical standards.
• Reporting and Workflow Tools: Helps platforms efficiently generate reports or automate administrative tasks like customer service for donor

At the forefront of the argument for integrating AI in fundraising is that machine learning technology stands to revolutionize workforce efficiency. Gravyty is a donor marketing and outreach tool that auto-generates emails based on repetitive learning the software has performed off of the organization’s donor data. Gravyty claims its tool empowers fundraisers to do four times more personal outreach and inspires first-time donors at a scale previously unthinkable. The software capabilities align with the “Online Fundraising Campaigns” model mentioned above; the donor relationship is the focus, instead of the actual identification of the donor. 

Along the lines of workforce efficiency, AI-equipped fundraising software stands to eliminate mundane administrative tasks and help direct more attention on strengthening donor relationships and strategies. Boodle.AI’s platform trains donor prediction model algorithms using third-party data, a 500-something data points across a 220-miilion-record data set. This includes demographics, lifestyle, and behavioral analytics to help the fundraiser make decisions. Similar to Gravyty, Boodle.AI seems to prioritize marketing data and consumer behavior in its training model. Their platform even offers a Predictive Advertising module, which uses a combination of data enrichment, AI modeling, and segmentation to micro-target consumers. 

Donor Prediction Models and Automated Stewardship

You may have heard of the “spray-and-pray” fundraising approach, a term coined by Gregory Warner. It’s a strategy where the fundraiser sends out a mass of donor appeals (“spray”) and proceeds to wait (“pray”) so that enough donors respond so they can make their goal. The spray and pray approach is transactional, expensive, and ineffective. Retention rates fall and donors lapse from burnout. Yet, moving to a targeted and focused fundraising approach can be time-consuming and tedious. Combing through prospect lists and current supporters can take hours and weeks of analysis.

This task might take an experienced fundraiser a week of tedious desk work, but the algorithm can accomplish it in minutes. The fundraiser is freed to use that time for face-to-face meetings with donors, delving deeper into their interests and needs to personalize cultivation and an ask.

Beth Kanter and Allison Fine, AI4Giving

This is where another facet of AI tech is altering the fundraising workflow: donor prediction models and automated stewardship. In this capacity, machine learning is used to train algorithms on donor data to identify prospects and predict upcoming donor lapses. These providers typically integrate with existing customer relationship management databases. 

Product Review

Introducing, Dataro. The “GiveTech” startup’s propensity modeling software uses machine learning technology to generate predictions about how donors are likely to behave. Dataro’s AI software aims to maximize income and minimize wasted costs, and transform the way that fundraisers stay alert and relevant to how donors are engaging with their organizations. Back in the product’s early development, it was selected as one of the top 10 fundraising innovations for not-for-profits in a competitive global open innovation challenge in 2020. Winning the competition built momentum for Dataro’s international plans. Dr. Tim Paris, David Lyndon, and Chris Parver, co-founders of Dataro, were able to raise $2.5 million in seed funding by 2021. With over 100 clients between Australia and the U.S. after three years of operations, Dataro’s rise certainly should be noted. 

The Technology & Model

Here is how the process works with Dataro’s machine learning tools: 

1. The machine learning software analyzes the fundraising data, splitting it into hundreds of discrete “features” like gift recency and donor age.
2. The AI searches through these features to find trends, patterns, and relationships. It uses these insights to begin training algorithmic models that can make predictions for the specific desired propensity.
3. The software evaluates its trained models to find the one that performs the most accurately based on all the complex relationships in the organization’s historical data. 
4. This model is used to generate propensity scores and ranks for each individual donor, showing how likely they are to behave in a certain way and how they compare against the rest of the donors in the database. 
5. These scores and ranks are exported back to the organization’s CRM dashboard for easy use and analytic metrics, giving a real-time AI-driven list of the ideal donors who should be contacted. 

Differentiating Dataro

Let’s differentiate Dataro from competitors: 

• Unlike Boodle.AI, Dataro does not augment data from external third-party sources. Dataro’s predictive models are only trained using the nonprofit’s data. This ultimately takes the guesswork out of fundraising. 
• Dataro is not a wealth screener. It doesn’t generate artificial wealth scores to grasp a prospect’s capacity. The software predicts donor behavior to help fundraisers prioritize attention on specific donors.    
• Having originated in Australia, Dataro’s system is built around compliance with the Privacy Act of 1988 and the Australian Privacy Principles. The company encourages and uses anonymized, tokenized data wherever possible, preventing an organization’s data from potential data breaches. 

Getting Started with the Software

To better understand the interface and technology, Dataro offers a free-of-charge demo trial with mock organizational data. I will use the demo trial as a basis for my product review below. 

Branding and UX Design

When I first opened the Dataro on my laptop, I was immediately impressed with the branding aesthetic and interface design. In terms of its visual identity, the pastel color palette and vector graphics distinguish Dataro as an approachable and brand-conscious platform. Moving around the software feels as if I’m scrolling through my phone. There are no obtrusive pop-ups, delays, or distracting background information. It doesn’t feel like I am navigating an AI-equipped donor platform, and I think that is intentional. 

Campaign Results

Under Campaign Results, Dataro users can track basic data such as total raised, net return, return on investment, response rate, and average gift size. For the purpose of this review, I chose the June Direct Mail Appeal to analyze, which has yielded a fantastic ROI of 155% and a response rate of 8.7%. 

Moving further down the page, Dataro evaluates its prediction results. Dataro scores approximate the probability of a patron doing a particular action, and the higher the score, the higher the probability of an individual taking the associated action. From the graph below, 79.63 percent of patrons with a 90 to 80 percent score responded to the DM appeal, which is just under the threshold of 80 percent. From the trend of the graph, it appears that the model just slightly underperformed in each score band. 

Dataro ranks order the organization’s donors from most to least likely to take a particular action. A person with Rank 1 is the most likely to act. Here, the graph illustrates that the individuals Dataro ranked from 1 to 1,000 had a collective response rate of 45.5 percent.

We can dive deeper into this data with Dataro’s prediction segments, listed in the tables below. Dataro gives us insight into the average gift amount, gifts over $500, and the largest gift of those top-ranked 1,000 individuals.

My favorite graph in the Dataro app is the Dataro Score Band by Net Returns. This graph illustrates where the organization starts to lose money on its ranked individuals. In this case, supporters ranked 4,000 to 9,815 yielded negative returns for the organization. 

The DM Appeal report is just the beginning of Dataro’s impressive array of propensity models. These include a Regular Giving (RG) Churn report, which measures the likelihood that a regular giver will churn/attrite in the next six months. The RG Upgrade, RG Reactivation, and Convert to RG propensity models are all available within Dataro to support the organization’s Regular Giving. Each report contains a similar dashboard as the DM Appeal campaign, a results table, and a table with results segmented using relevant segments from your CRM. 

Takeaways: The Campaign Results tab demonstrates Dataro’s accessible design and user-friendly interface. I do wish there was an option under each campaign to view the names of campaign supporters. The dashboard gives a substantial overview of the successes and failures of the campaign, but nonprofit fundraisers often operate from a degree of familiarity and name recognition. It would provide a more holistic and individualized outlook on the campaign and lead fundraisers where to focus their time and money. This isn’t a major issue, since we can see each individual’s rank and score through the Dataro dashboard within the organization’s CRM, which I will cover soon. 

Prediction Insights

The Prediction Insights tab provides a summary of each campaign’s predictive model, including the score band, the number of supporters in that band, and the percentage makeup of that band in the total cohort. For the DM Appeal campaign, four individuals are 90 to 100% likely to give to a DM appeal, just .01% of the cohort. Dataro’s machine learning pipeline is detailed below. 

Takeaways: The Prediction Insights tab could also benefit from a “See Who” button to list the individuals that Dataro has generated using the predictive model. Especially in a Churn, Activation, or Upgrade campaign, the donor most likely to act is a digestible volume of data and names for the fundraiser to use. 

Model Performance

Dataro has a system of checks and balances to test the quality and accuracy of their models. The Model Performance tab details the prediction quality and top predictors for each campaign. The DM Appeal earned a 96% stellar grade according to Dataro’s evaluation, and the model was trained mostly on the frequency of DM gifts, payment method, and total single giving.  

In true deep learning nature, the machine learns from the organization’s data. Each Dataro-trained model is unique to the organization’s data and unique to the moment in time. Considering how people are motivated at different times in the year to give (ex. tax season), it’s highly advantageous that Dataro constantly retrains its models to provide the most accurate predictions at each point in time. Dataro’s models are constantly self-adjusting, eliminating mistakes that didn’t accurately predict the right likelihood of action. 

Takeaways: It’s impressive to see such transparency behind Dataro’s modeling techniques. I wonder how the volume of available data dictates each model’s performance evaluation. The DM Appeal had a training data size of over 8.7 million, with 1.9 positive cases and 362 total predictors. The data training size isn’t that large for other evaluations, but it still begs the question: will Dataro’s models be as effective on my organization’s smaller data set? More on this below. 

Putting a Name to a Propensity Score

Dataro’s CRM integration is where the data starts to come to life. In the organization’s CRM, Dataro’s dashboard appears on the donor’s contact page. The above example is within Salesforce. Here, we can see the propensity scores that are only relevant for the specific contact. Propensity scores are listed on the left (on a scale of 0 to 1) and measure the individual’s likelihood to take a certain action. The propensity ranks, listed on the right, compare the individual’s likelihood to take the action against the entire cohort of donors.

When the fundraiser begins to initiate a campaign, they will want to create a report in their CRM using Dataro’s propensity scores. In the video below, Dr. Chris Parver demonstrates the simplicity behind creating a list based on the top 500 ranked individuals likely to give to a DM appeal. Dataro’s models and predictions are also dynamic, updating weekly so the organization will always get the most accurate predictions in real-time.

Takeaway: I cannot evaluate the effectiveness of Dataro's CRM integration capability as I am using the free trial software and not any organizational data. From Dataro's Help Desk videos, it seems like accessing the donor's scores and ranks on their contact page help the fundraiser make meaning of the data. It's important to remember that these scores are predictions and not guarantees of a donor's behavior. 

Data Requirements and Integrations

AI and machine learning are only as good as the data they are working with. Machine learning algorithms require large volumes of data in order to learn patterns and behaviors, and to create models based on that analysis. Prospective nonprofits hoping to utilize Dataro or other AI technology may be at a standstill if they don’t have enough usable data. 

For Dataro to achieve predictive accuracy, the client needs at least five years of giving history, and at least 10,000 constituent records. Those records don’t all necessarily need to be active, but it is crucial for the system to work on a substantial amount of active data. 

Dataro currently works with the following CRM systems: Donorfy, Salesforce, ThankQ, Blackbaud, Raiser’s Edge, NXT, Microsoft Dynamics, and Advanced NG. 

Takeaway: Looking through Dataro's current clients, the case studies are powerful applied examples of the technology Dr. Paris said the Royal Flying Doctor Service Victoria increased net revenue in their 2020 Tax Appeal by nearly $35,000 by using Dataro’s propensity scores. But I'm surprised to see very few, if not any artistic clients listed. After speaking with Debbie Stanley, Leader of U.S. Expansion at Dataro, it doesn't seem that Dataro has partnered with many arts organizations. This is in part due to their very recent expansion to the U.S. 

I see contradictions between Dataro's mission to democratize the use of AI technology and the limitations inherent within the nonprofit infrastructure. The current CRM systems that Dataro integrates will not work with the systems of many nonprofits. Even if nonprofits have the correct integrated software, the organization likely will not have enough active data records. These issues point to how the emerging use of AI for philanthropy may potentially skew towards better-resourced platforms and nonprofits, bringing AI inequities to mind. 

Conclusion

Researching Dataro gave me a certain appreciation for AI-equipped fundraising software that focuses on donor identification. Using donor prediction models puts the fundraiser first and the AI second. AI-powered donor communication systems, on the other hand, do the opposite. I would not advocate for a nonprofit to begin using AI-equipped donor communication tools for several reasons. The ethics of using AI for donor solicitation are tricky to navigate considering authorship issues. Additionally, nonprofit staffs are typically their own experts on donor messaging. Between acknowledgment letters, email campaigns, and day-to-day communications, professional fundraisers know how to speak with their donors. It is the decision-making, tasks like optimizing a list of 5,000+ potential donors for solicitation, that development staffs stand to benefit the most from.

I see Dataro as a revolutionary force in nonprofit fundraising. The technology, if truly effective with the right organization’s data, harnesses the potential to shape how we prioritize our time with donors. At this point in time, Dataro is an elite solution. If the product can scale to organizations with smaller datasets and a broader selection of CRM software, then the future is bright for this tech startup.

AI is like hot sauce. A little bit, used in the right place, can be perfect. But dumping too much on or using it everywhere will probably ruin your meal.

Nick Hamlin, AI4Giving

My advice to organizations curious about Dataro or another AI-equipped fundraising platform: approach AI as an experimentation strategy with evaluation metrics baked into the process. We experiment with ideas and projects that are never fulfilled. If the experiment indicates growth for your organization, then move forward and implement the technology into your existing system. AI should not completely reinvent the nonprofit fundraising wheel.

Bibliography

Australian Government. 2014. “Privacy Act 1988.” Legislation.gov.au. Attorney-General’s Department. March 12, 2014. https://www.legislation.gov.au/details/c2014c00076.‌

Bindbeutel, Duncan. “Navigating the Ethics of Using AI for Donor Solicitation.” AMT Lab @ CMU. April 20, 2022. https://amt-lab.org/blog/2022/4/how-can-nonprofits-navigate-the-ethics-of-using-ai-for-donor-solicitation.‌

“Boodle for Nonprofits.” BoodleAI. Accessed April 22, 2022. https://boodle.ai/nonprofits/.

“Dataro Demo Account.” Dataro. Accessed April 22, 2022. https://www.app.dataro.io/home

“FAQ – Dataro.” Dataro. Accessed April 22, 2022. https://dataro.io/faq/.

“Fundraising Powered by AI – Wisely Product.” Wisely. Accessed April 22, 2022. https://fundraisewisely.com/wisely-ai-powered-fundraising-software-product/.

“Gravyty First Draft.” n.d. www.gravyty.com. Accessed April 22, 2022.    https://www.gravyty.com/firstdraft/.

Kanter, Beth, and Allison Fine. 2020. “AI4Giving: Unlocking Generosity with Artificial Intelligence: The Future of Giving.” Bill & Melinda Gates Foundation.

Martel, Adam. 2021. “How Artificial Intelligence Is Transforming Fundraising.” Education and Career News. March 7, 2021. https://www.educationandcareernews.com/future-of-higher-education/how-artificial-intelligence-is-transforming-fundraising/.‌

Alex Moltzau. 2019. “Artificial Intelligence and Nonprofits.” Medium. September 1, 2019. https://towardsdatascience.com/artificial-intelligence-and-nonprofit-e6cdaaae166f.‌

Parver, Chris. 2021. “Fundraising Analytics: 20 Critical Metrics & How to Use Them – Dataro.” Dataro. March 25, 2021. https://dataro.io/2021/03/25/fundraising-analytics/#future.‌

—————.“Help Desk – Dataro.” Dataro. https://support.dataro.io/hc/en- us/articles/1500004945662-What-are-the-definitions-for-each-Dataro-model-.

—————-. “How Do I Tell If Dataro’s Predictions Worked? – Dataro.” Dataro. 2021. https://support.dataro.io/hc/en-us/articles/1500004945662-What-are-the-definitions-for- each-Dataro-model-. 

—————.  “Minimum & Suggested Data Requirements– Dataro.” Dataro. 2021. https://support.dataro.io/hc/en-us/articles/1500004945662-What-are-the-definitions-for- each-Dataro-model-.

Paris, Tim. 2022. “Beat the Machine by Dataro.” GoToWebinar. Presented at AI for Fundraising: Learn from an Expert, April 19. 

Rizal, Paulo. 2020. “Web Login Service – Stale Request.” December 16, 2020. https://courses.ideate.cmu.edu/62-830/s2022/wp-admin/post.php?post=1415&action=edit.‌

“Predictive Advertising for Modern Agencies and Brands.” BoodleAI. Accessed May 6, 2022. https://boodle.ai/predictive-advertising/.‌

Stanley, Debbi. 2022. Dataro Product Demo Meeting Interview by Evan Aanerud. Dataro.

Thomsen, Simon. 2021. “AI-Led ‘Givetech’ Dataro Lands $2.5 Million Seed Round to Help        Charities Innovate Fundraising.” Startup Daily. December 16, 2021. https://www.startupdaily.net/2021/12/ai-led-givetech-dataro-lands-2-5-million-seed-        round-to-help-charities-innovate-fundraising/.

Warner, Gregory. 2019. “3 Simple Steps to Break Your Transactional, Spray-And-Pray Fundraising Addiction.” www.linkedin.com. September 10, 2019. https://www.linkedin.com/pulse/3-simple-steps-break-your-transactional-fundraising-addiction-warner-1e/.‌

“What Are Australian Privacy Principles | Privacy Act Guidelines | Imperva.” n.d. Learning Center. https://www.imperva.com/learn/data-security/australian-privacy-principles/.‌

“10 Must-Have Features to Have on an AI-Driven Fundraising Platform.” 2019. BoodleAI. September 19, 2019. https://boodle.ai/blog/artificial-intelligence/10-must-have-features-on-ai-driven-fundraising-platform/.

Introduction

As I quickly approach my graduation from one of Carnegie Mellon University’s data analytics programs, I’m encouraged to reflect on the key lessons I will take into the work force. One consistent lesson is what it takes to incorporate Artificial Intelligence (AI) into an organization: Carnegie Mellon University (CMU) was ranked number one in 2022 for Artificial Intelligence Programs by U.S. News and World Report. Incorporating CMU’s AI lessons is critical for all industries but particularly those that are historically considered to be “non-technical fields.” Within these fields, the ones who thrive will be those who incorporate at least the lower levels of the stack, namely data management, but an understanding of each layer of the stack will provide industries with a road map to understand how they can increase the efficiency and output of their organization’s business processes.

Explanation of AI Stack

As technology becomes an increasingly large part of the workplace, organizations are encouraged to engage in “full stack” operations. At its heart, this means that organizations should work through developing each level as shown below:

While there are MANY definitions and paths to achieving artificial intelligence within an organization, Carnegie Mellon defines AI as understanding human needs and making “…smart decisions based on that understanding.” Each of the levels in the diagram is achieved through iterative developments. Below is a brief definition of each layer of the stack and key considerations for the arts to consider within each.

Computing & Devices

Defined as “…the collection of computer machinery, data storage devices, and networks that support the processing and exchange of electronic information,” the computing environment is likely the electronics within an organization that everyone will interact with and touch. If computers are too slow, data is strictly stored in old servers, etc, this can cause a shaky foundation for the rest of the stack. MIT defines three areas that make computing and devices different with AI in mind:

1. Carry out calculations in parallel – “speed” historically is how fast a computer could perform a single task (for example, loading a web browser). But now, computers are expected to load a web browser in the foreground while updating a database in the background. Talking to the organization’s IT team about the capabilities of devices (particularly in terms of parallelization) is important in determining the technology expansion capabilities of the organization.

2. “Learning” rather than listening – Historically, computers took a single input from users and executed a task. When we think about devices in the context of the AI stack, however, devices are now expected to learn. In personal devices, this takes the form of voice recognition (Siri) and image recognition (unlocking an iPhone using facial recognition). The application of this in the art world is a greater application to recognize images and sounds. For example, with the proper devices and computing capabilities, museums can implement technology such as determining if a piece of artwork is a fake work or not.

3. Computerization of the commonplace – Think of this aspect of computing and devices in terms of a “smart house.” Modern infrastructures (i.e. storage of data on the cloud) enables things as simple as a doorbell to expand their capabilities to become “smart” technology. If smart technology has a place within an industry, it’s important to first identify if the computing and devices infrastructure can support this change.

Data Management

Data Management is critical to business success. Higher level processing and productivity within an organization cannot happen without proper data management, so in many ways, data management is the most critical level of the AI stack. Data management is defined as “… the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.”The path to proper data management for different organizations can take many forms, but below is an example process for establishing data management:

The key to data management ultimately comes down to one word:

VALUE

Data management has shown to bring organizations increased value by streamlining process and revealing where to focus business strategy. In the art industry, this could mean greater customer analytics within an art museum to see which populations visit most often, and therefore which populations to target with promotions and deals. For example, if detailed data management and analytics reveals that families with kids between the ages of 2 and 12 make up the majority of museum visitors over the summer, the museum could focus its displays and programs during those months on catering to that population.

Beyond the art industry, civic organizations and local governments have already shown how much value proper data management can generate: by creating the infrastructure for open source data management, the public sector “… has sparked innovation, driven efficiency, and fueled economic development.” Imagine the possibilities for the art industry if more art organizations do the same.

Machine Learning and Modeling

Machine Learning (ML) is an over-used and often misunderstood term. ML is defined as “…the systematic study of algorithms and systems that improve their knowledge or performance with experience” (Akoglu). In plain terms, ML and modeling are advanced math applied to data using computers. The most promising applications for ML in the art industry currently are the automation of business processes (see below), but there are also many examples for how ML is being used to GENERATE art. For more information on this, check out this Ted Talk on music and art generation using machine learning:

Decision Support, Planning, & Acting

This portion of the stack refers to systems that have surpassed the functionality of modern machine learning. The decision support layer simply “helps humans make decisions,” while the planning and acting layer “…rely on optimization, safety, the knowledge network, and strategic reasoning” to learn from information they are given. It’s difficult to understand why and how this layer is different from ML and AI (or why it fits in between the two layers in CMU’s AI stack), but this layer teaches us that the level of task automation and computer “learning” exists on a spectrum.

Autonomy / Human AI Interaction

Human AI interaction is defined as “… the completion of a user’s task with the help of AI support.” While AI can often be an intimidating concept and mistrusted by those who are unfamiliar with it, it has many potential applications within the art industry for automating production, identifying and maintaining consumer markets, and other task completion and automation.

Ethics

A discussion of the AI stack would be incomplete without stressing the importance of ethics in AI development decisions. Beginning with the data management layer, the art industry has the potential to store and track large amounts of personal data on its customers (similar to other consumer industries). The art industry is largely trusted by its consumers, so as its capabilities within the stack expand, ethical design is critical. Large companies within the art industry have identified its necessity and have started to build infrastructure around ethical decision making which can provide a guide for other smaller organizations. For example, Spotify created an Ethics Guild to make ethical considerations “more concrete.” They created a simple Google document open for other organizations to use that evaluates the decisions within the AI and technology stack that different groups within an organization are making as shown below:

How to Bring ‘the Stack’ to the Arts

1. Increase data literacy

Data literacy is defined as the collection, examination, analysis, and interpretation of data to inform a decision. Using data to inform decisions is one of the most important first steps an organization must take before considering any higher parts of the stack. Organizations can do this by heavily democratizing data (ensuring all members of the organization have access to it) and increasing ease of use whenever possible.

To be clear, data democratization is not synonymous with data anarchy; not every member of the organization should have access to all the data. For example, the managerial and janitorial staff of a concert hall may not need access to detailed demographic data on the concert hall’s visitors and guests. However, it would be very useful for them to have visitor frequency data (what times of the year or what types of shows bring in the most visitors) so that they can plan accordingly for peak bust times.

2. Use art itself

In 2016, an education study was conducted in Brazil that looked at how to increase data literacy in young children. After testing different methods, the research team found that those students who learn using visual arts activities performed far better in tests than those who learned through traditional educational methods. The take-away? When looking at how to bring data literacy and eventually the stack to arts workplaces, use art! It’s proven that people respond best learning a new concept through creative methods.

One of the researchers, Catherine D’Ignazio, identified tactics that could help to build data literacy[2] that included building learner-centric tools and favoring creativity over purity. Teaching technical concepts doesn’t require intensely ‘academic’ verbiage or techniques.

Data literacy is intimately intertwined with increased use of visualizations to communicate results. To generate change within an organization or inspire people to action, visualizations are often the best way to captivate an audience. Consider the visualizations below:

Although beautiful in its colors and layout, the visualization has a specific purpose: to educate people on ways child mortality has been reduced historically. Visualizations appeal to a wide range of emotions (similar to art), and often provide information to viewers that would be lost in traditional reports. For example, if you are a manager for a music production company, creating visualizations that show declining sales in the form of a descending scale on a piece of sheet music, this could grab the attention of the audience more because it is shown in a familiar medium. The visualization options are vast, and the benefits of incorporating art into data management strategies are endless.

3. Automate when possible

During the pandemic, 42% of art organizations had to decrease the number of people on their staff, but 65% were still delivering some sort of content. The arts industry must adopt some form of technology for administrative tasks and distribution in order to stay afloat in an increasingly digital world.

Business automation tasks include the following:

• Responding to emails – when it comes to customer feedback, develop a system to automatically sort and respond to any correspondence an organization receives
• Data management functionalities – depending on the data collection and storage methods used by an organization, data “pulls” and back-ups can be easily automated using out-of-the-box software systems
• Customer engagement – social media posts, emails, and other promotions can be setup on a time schedule so that employee time is not taken up by maintaining a customer engagement schedule

There is a plethora of task automation tools available on the internet, and it is important to develop time to determining the best fit between software systems and the organization.

Conclusion

“I say, run toward the data. Take your data back and turn it into something meaningful.”

Laurie Frick, From Paint to Pixels

The utility of the AI stack is evident: the organizations that choose to implement it will succeed, while those who fail to implement will likely cease to exist. Although it seems overwhelming to implement the stack within the art industry, organizations can start simply by focusing on the data. Strive towards data literacy and education. It may be the decision that saves the arts.

References:

“7 Steps to Foster a Data-Driven Culture for Your Organization.” (February 18, 2021). Unscrambl. Retrieved March 8, 2022 from https://unscrambl.com/blog/7-steps-to-foster-a-data-driven-culture-for-your-organization/

Akoglu, Leman. “Lecture 1: Machine Learning: Basic Concepts.” (Spring 2019). Course Notes for 95828: Machine Learning for Problem Solving.

“AI Stack” (2022). Carnegie Mellon University. Retrieved May 3, 2022 from https://ai.cs.cmu.edu/about

Baker, Melissa (Dec 27, 2019). How to Create a Technology Adoption Strategy that Works. Burwood Group. Retrieved March 8, 2022 from https://www.burwood.com/blog-archive/technology-adoption-strategy

Batti, Khadim (Feb 6, 2020). How Technology Could Impact the Workplace Over the Next Decade. Forbes. Retrieved March 8, 2022 from https://www.forbes.com/sites/forbestechcouncil/2020/02/06/how-technology-could-impact-the-workplace-over-the-next-decade/?sh=620396fb3bd2

Berkel, Niels van, Mikael Skov, and Jesper Kjeldskov (Nov-Dec 2021). “Human-AI Interaction: Intermittent, Continuous, and Proactive.” Interactions. Retrieved May 4, 2022 from https://interactions.acm.org/archive/view/november-december-2021/human-ai-interaction

“Best Artificial Intelligence Programs” (2022). U.S. News & World Report. Retrieved April 26, 2022 from https://www.usnews.com/best-graduate-schools/top-science-schools/artificial-intelligence-rankings

Bhargava, Rahul, Ricardo Kadouaki, Emily Bhargava, Guilherme Castro, and Catherine D’Ignazio. (Oct 22, 2016). Data Murals: Using the Arts to Build Data Literacy. The Journal of Community Informatics. Vol. 12, No 3. Retrieved March 8, 2022 from https://openjournals.uwaterloo.ca/index.php/JoCI/article/view/3285

Chambers, Rick (April 27, 2021). Data Democratization just sounds too buzzy. Medium. Retrieved May 4, 2022 from https://2rickchambers.medium.com/data-democratization-just-sounds-too-buzzy-b6e98f001909

Goldstein, Brett and Lauren Dyson (2013) Beyond Transparency: Open Data and the Future of Civic Innovation. Published by America Press, San Francisco. Retrieved March 8, 2022 from https://beyondtransparency.org/

Gummer, Edith S. and Ellen B Mandinach (April 1, 2015). Building a Conceptual Framework for Data Literacy. Teachers College Record. Vol 117, Issue 4. Retrieved March 8, 2022 from https://journals.sagepub.com/doi/abs/10.1177/016146811511700401

Kefford, Matt (Sept 9, 2021). How Can Technology Save the Arts? Business Because. Retrieved March 8, 2022 from https://www.businessbecause.com/news/insights/7813/technology-arts-after-covid

Han, Lu, Eric Madar, and Sara Aboulafia (Sept 2020). “Investigating Consequences with Our Ethics Assessment.” Spotify. Retrieved May 4, 2022 from https://spotify.design/article/investigating-consequences-with-our-ethics-assessment

Haven, Will Douglas (October 22, 2021). “How AI is reinventing what computers are.” MIT Technology Review. Retreived May 3, 2022 from https://www.technologyreview.com/2021/10/22/1037179/ai-reinventing-computers/

Maycotte, H.O. (Oct. 28, 2014). Data Literacy – What it is and Why None of Us Have It. Forbes. Retrieved March 8, 2022 from https://www.forbes.com/sites/homaycotte/2014/10/28/data-literacy-what-it-is-and-why-none-of-us-have-it/?sh=4dd5738168bb

Rampton, John (July 27, 2020). “14 Business Tasks That Can Be Automated.” Calendar. Retrieved May 4, 2022 from https://www.calendar.com/blog/14-business-tasks-that-can-be-automated/

Schmidt, Richard F (2013). “Software Architecture.” Science Direct. Retrieved May 3, 2022 from https://www.sciencedirect.com/topics/computer-science/computing-environment

Simpson, J (February 17, 2020). 6 Inspiring Examples of Data-Driven Art. Nordicapis. Retrieved March 8, 2022 from https://nordicapis.com/6-inspiring-examples-of-data-driven-art/

Stedman, Craig and Jack Vaughan. “What is data management and why is it important?” TechTarget. Retrieved May 4, 2022 from https://www.techtarget.com/searchdatamanagement/definition/data-management

Thomson, Kristin, Kristen Purcell, and Lee Raine. (January 4, 2013). Overall Impact of Technology on the Arts. Pew Research Center. Retrieved March 8, 2022 from https://www.pewresearch.org/internet/2013/01/04/section-6-overall-impact-of-technology-on-the-arts/

“The Top 5 Biggest Advantages of Data Visualization.” Salesforce. Retrieved May 4, 2022 from https://www.salesforce.com/ap/resources/articles/data-visualization-advantages/

Urist, Jacoba (May 14, 2015). From Paint to Pixels. The Atlantic. Retrieved March 8, 2022 from https://www.theatlantic.com/entertainment/archive/2015/05/the-rise-of-the-data-artist/392399/

“What is Data Management?” Oracle. Retrieved May 4, 2022 from https://www.oracle.com/database/what-is-data-management/

Wroblewski, Alyssa (June 2, 2020). The Present and Future of Arts Organizations, Technology, and the Pandemic. Arts Management and Technology Laboratory. Retrieved March 8, 2022 from https://amt-lab.org/blog/2020/5/the-present-amp-future-of-arts-organizations-technology-and-the-pandemic

“Preserving cultural heritage isn’t something that is solely nice to have or nice to do, it’s sometimes imperative to the well-being of the world’s societies.”

– Brad Smith, the President of Microsoft

According to Poonam Gandhi, a professor at Flame University, culture and heritages shape human society and provide a sense of identity. Specifically, cultural heritages can stimulate tourism, which hence benefits the local economies, educate people with culture and history, and enhance social inclusiveness.

The importance of preserving cultural heritage is obvious, meanwhile, people are facing difficulties in the preservation process. So, what can AI and other modern technologies actually do in the process of preserving cultural heritages that makes Microsoft and other organizations invest in them?

Antiques Restoration

Due to natural disasters or man-made damages during the war, lots of precious historical heritages had been broken into pieces. Considering the time and cost efficiency, plus the difficulty of lacking pieces of evidence of their original looks, it’s very difficult to repair those historical artifacts and might need the efforts of several generations. Just like solving a big jigsaw puzzle without knowing what it should look like, everything starts from scratch and guesses. 

RePAIR

The ancient Roman city of Pompeii which was destroyed and buried by the eruption of Mount Vesuvius in A.D. 79, is now only left with the incomplete architecture and thousands of irregular fragments of ancient artifacts, such as vases and frescoes.

The RePAIR (Reconstructing the Past: Artificial Intelligence and Robotics meet Cultural Heritage) project is proposed by a group of scientists at the Italian Institute of Technology (IIT) to help with the time-consuming and labor-costly physical reconstruction process of Pompeii restoration. The technologies involved in the project are robotics, 3-D scanning, machine-learning algorithms and artificial intelligence. 

The first component is about scanning the fresco puzzle pieces using high-tech computers, using machine-learning algorithms to solve the puzzles, and having human expertise to guide the AI at the same time.

A team of archeologists from the University of Lausanne, who have attempted to solve the puzzle in the past, along with other professional art historians and scholars input their current knowledge about the Pompeii puzzles into the AI algorithm, then let it evolve and study by itself based on those examples, and apply to a larger scale. The computer software will compare all the fragments in pairs and evaluate their degree of similarity based on the shapes, the fitness, and how the illustrations on the pieces match up, which helps draw a picture of what the original complete piece looks like. 

The second component brings robots into the workplace. The robot should be able to scan those pieces on its own, and move the fragments together as a complete one based on the paired results generated by AI. In the end, it will reassemble the pieces together into a finished “puzzle”. Designed by Nikolaos Tsagarakis and his colleagues at the Humanoid & Human Centered Mechatronics Lab at IIT in Genoa, the robot will look like an arm that has a similar size as an average person, weighing 25 to 30 kilograms and lengthening 80 to 100 centimeters. A soft glove hand will be attached to the arm to grab and move the fragments with extreme care, and gather information about them while holding them in hand. The robot will help save a large amount of time and human resources. It doesn’t need to sleep or eat, but can work 24 hours a day without rest. Antonio Bicchi, a senior scientist at IIT in Genoa and the chair of robotics at the University of Pisa is in charge of the design of the robot’s hand, and says the ideal version of it in the future will also be able to allow humans to wear them and gather data during the process.

The project and its techniques are still at the experimental stage. The two experimental subjects will be the frescoes from the ceiling of the House of the Painters at Work, in the Insula of the Chaste Lovers, and the frescoes from the Schola Armaturarum. At the end of the project, the finished frescoes will be exhibited to the public for visitors to experience ancient Pompeii and for researchers to study. And this is the first time in the archeological field that AI will be used at such a large scale, and robotic hands will be responsible for such a large amount of pieces.

Therefore, the progress it can contribute to cultural heritage restoration and human history is significant and meaningful. “If the project succeeds, the technology can be used to reconstruct a variety of fragmented cultural artifacts whose restoration has remained ‘out of human reach’, ” said the project coordinator Marcello Pelillo, a professor of computer science and artificial intelligence at the University of Venice.

The RePAIR scientists aim to install the robot in Pompeii by the spring or summer of 2022. And hopefully, this project will succeed and benefit other cultural sites in the world that need to be repaired.

Scan4Reco

Another EU-funded project called Scan4Reco is also a great example of using artificial intelligence-assisted technologies for cultural heritages’ preservations.

They first use depth-scanning cameras to create a high-resolution 3D replica to transfer the artifact into digital files. Then, more information about the object’s underlying layers which cannot be witnessed directly by human eyes is added to the research by the sensor data.

“Very specialized sensors used in the conservation domain carry out non-destructive diagnostic analysis of the different layers that is equivalent to the more destructive chemical ones,” the deputy project coordinator Dr. Anastasios Drosou explains.

The sensor data is combined with predictive modeling technologies using AI, whose deep learning algorithms simulate the artifact’s appearance as it ages. Therefore, it can suggest conservators with accordingly treatments to protect the artifacts before their conditions get more terrible, or to minimize the existing damages and keep their current looks. According to Dr. Drosou, when the system detects a scratch or some color fading in a painting, the system would advise the conservators to apply a particular chemical on the scratch, or paint or cleaner in the other area.

Digitalization

Another beneficial and impactful progress that technology brings to the field is the digitalization of cultural heritages, where laser scanning and photogrammetry play essential roles. Having digital cultural heritage will help record history, establish a digital library for researchers and future generations to learn about them, and enrich cultural vibrancy.

The Mogao Caves, located in Gansu province in northwestern China, with over 2,000 sculptures and 45,000 square meters of unique and fragile painted murals, all of which are between 600 and 1,600 years old, is facing the challenge of preserving them in their current conditions. 

The caves are great demonstrations of the history of the Silk Road as well as related commercial trades and cultural exchanges across countries. However, due to thousands of years of natural disasters and damages, as well as the overloaded tourists after its development and reopening as a historical visiting site, the speed of painted statues damage and fresco degradation of Mogao Cave is dramatic.

To better preserve this UNESCO World Heritage Site, the researchers and scientists in China decided to use Laser Scanning and Multi-baseline Rotating Photogrammetry to turn the statues and frescos into digital formats, and store them in a safe data library for future studies and possible virtual exhibitions to let people appreciate the caves on their mobile devices with the similar experience as if they were in the caves.

• Photogrammetry: a sophisticated process by which information is extracted from photographs to create accurate three-dimensional maps and models. Using ultra-high-resolution aerial photographs, this practice combines UAV-mounted overhead sensors with powerful GIS mapping systems to create dynamic, measurable documents for a number of real-world situations and uses. (Source: Science Direct)

• Laser Scanning: an automatic, direct measurement of 3D points that can accurately measure and collect data from objects, surfaces, buildings, and landscapes. Laser scanners collect information in the form of point cloud data, which consists of millions of 3D coordinates (XYZ coordinates). (Source: Science Direct)

Another historical site that has been damaged and needed further protection and repair is the Notre-Dame Cathedral which was burnt by fire in 2019. The government is planning for its reconstruction and says hoping to finish by 2024.

Thanks to the digital scanning that they previously did before the fire, the team are now able to create a 3D BIM model of the Notre-Dame Cathedral as it existed before. Then, using recent reality capture scans to create the post-fire 3D model using a 3D design software called Autodesk, and comparing these two versions to rebuild the Cathedral.

Since the roof which was the most burnt part of the Cathedral was made of wood, this cultural tragedy rings the alarm for other historical buildings with wood structures. Performing 3D scanning and modeling to have digital forms of the heritages on file would be a great kind of look-ahead preparation for future possible accidents.

Conclusion

AI and other advanced technologies can help with the restoration and digitalization of tangible cultural heritage. They work as assistants of people on those works that cannot be achieved by human abilities or are time-consuming. They also work as tools for people to utilize and advance their working process to a higher level.

We’ve already seen some great progress that utilizing technologies in cultural heritages’ protections from the above projects and examples. In the future, we can also have a positive prospect that technologies would be able to make more achievements in this area by human inventions and under human supervision.

Bibliography

“AI for Cultural heritage.” Microsoft. Accessed February 25, 2022. https://www.microsoft.com/en-us/ai/ai-for-cultural-heritage.

“An Introduction to Drone Photogrammetry.” Mapware. Accessed March 4, 2022. https://mapware.ai/an-introduction-to-drone-photogrammetry/?gclid=CjwKCAiAjoeRBhAJEiwAYY3nDBfZ-wtHG1wd1Xv4sfiGfTEjObRb2Pm-CkaRFOXQycgG5RP7R-1R0BoCm-cQAvD_BwE#whatisutm_term=photogrammetry&utm_campaign=Traffic_Clicks_Global+%5BJoe%5D&utm_source=adwords&utm_medium=ppc&hsa_acc=7859616296&hsa_cam=15446499960&hsa_grp=136083379128&hsa_ad=566158081175&hsa_src=g&hsa_tgt=kwd319349969229&hsa_kw=photogrammetry&hsa_mt=p&hsa_net=adwords&hsa_ver=3.

Brandon, Elissaveta M. “How AI and Robotics are Reconstructing A 2,000-year-old Fresco in Pompeii.” Fast Company. December 23, 2021.

https://www.fastcompany.com/90708962/how-ai-and-robotics-are-reconstructing-a-2000-year-old-fresco-in-pompeii.

“Cutting Edge | Protecting and Preserving Cultural Diversity in the Digital Era.” UNESCO. October 28, 2020. https://en.unesco.org/news/cutting-edge-protecting-and-preserving-cultural-diversity-digital-era.

Denbo, Seth. “Digitalizing Cultural Heritage: Historic Preservation and The Mogao Caves in China.” Perspectives on History. December 1, 2016. https://www.historians.org/publications-anddirectories/perspectives-on-history/december-2016/digitizing-cultural-heritage-historic-preservation-and-the-mogao-caves-in-china.

“‘Digital Surrogates’ Assist in the Preservation of Cultural Artefacts.” CORDIS. February 19, 2019. https://cordis.europa.eu/article/id/248968-digital-surrogates-assist-in-the-preservation-of-cultural-artefacts.

Gandhi, Poonam. “World Heritage – Why Preserve, Conserve and Protect?” TravelWorld.com. April 20, 2021. https://travel.economictimes.indiatimes.com/speakeasy/world-heritage-day/4891.

“How Technology Is Preserving Our Cultural Heritage.” Reader’s Digest. Accessed February 25, 2022. https://www.readersdigest.co.uk/inspire/life/how-technology-is-preserving-our-cultural-heritage.

Horgan, Rob. “How Digital Technology is Aiding Notre Dame Rebuild, Two Years since Tragic Blaze.” New Civil Engineer. April 15, 2021. https://www.newcivilengineer.com/latest/how-digital-technology-is-aiding-notre-dame-rebuild-two-years-since-tragic-blaze-15-04-2021/#:~:text=In%20a%20bid%20to%20aid,existed%20before%20the%20catastrophic%20event.

Ibaraki, Stephen. “Artificial Intelligence For Good: Preserving Our Cultural Heritage.” Forbes. March 28, 2019. https://www.forbes.com/sites/cognitiveworld/2019/03/28/artificial-intelligence-for-good-preserving-our-cultural-heritage/?sh=bf2912f4e960.

“Laser Scanning Reveals Cathedral’s Mysteries | National Geographic.” National Geographic. June 23, 2015. Video, 4:10. https://youtu.be/jAi29udFMKw.

“Laser Scanning.” ScienceDirect. Accessed March 4, 2022. https://www.sciencedirect.com/topics/earth-and-planetary-sciences/laser-scanning.

Pinkowski, Jen. “Pompeii’s Ruins to Be Reconstructed by Robot.” Scientific American.December 6, 2021. https://www.scientificamerican.com/article/pompeiis-ruins-to-be-reconstructed-by-robot/.

“RePAIR Project.” Pompeii Sites. September 2, 2021. Video, 4:49. https://youtu.be/v9rpoLL5eLM.

“Reconstructing the Past: Artificial Intelligence and Robotics Meet Cultural Heritage.” CORDIS. Accessed February 25, 2022. https://cordis.europa.eu/project/id/964854.

“The RePAIR Project Begins – Robotics and Digitisation at the Service of Archaeology.” Pompeii. Accessed February 25, 2022. http://pompeiisites.org/en/comunicati/the-repair-project-begins-robotics-and-digitisation-at-the-service-of-archaeology/.

Werkheiser, Greg. “Artificial Intelligence: Cultural Heritage Required.” Cultural Heritage Partners. Accessed February 25, 2022. http://www.culturalheritagepartners.com/artificial-intelligence-cultural-heritage-required/.

“What Is Laser Scanning and How Can It Be Used?” TopoDot. June 11, 2020. https://blog.topodot.com/what-is-laser-scanning-and-how-can-it-be-used/.

Wiggers, Kyle. “Microsoft Launches AI for Cultural Heritage Program.” Venture Beat. July 11, 2019. https://venturebeat.com/2019/07/11/microsoft-launches-ai-for-cultural-heritage-program/.

We live in a world full of data. Data brings information, knowledge, and wisdom, as presented by the DIKW pyramids. Based on large sets of data, we can add value and enrich it through productive analysis, and finally, we can get wisdom. With technology developing, getting trends and insights from unorganized raw data becomes easier and easier for individuals and organizations. Today, most for-profit companies collect data and use analytical tools to target customers, reduce costs, and make optimal choices. As nonprofits, arts organizations also rely on databases to collect audience information and get a better understanding of them. People are still exploring ways to apply new technologies to analyze data precisely. As one of the hottest emerging concepts at the moment, AI can do more than simulate human behavior. In the article, we will explore how to integrate machine learning, a type of artificial intelligence that has strong analytical and forecasting skills, into art organizations, better “learn” from your data of audience and achieve their mission.

What is machine learning?

Machine learning is a branch of artificial intelligence that applies more to the data analysis field. One of its core concepts is to use machines to learn from data and make predictions. It is a way to “use computers to answer questions”. The computer will learn from a training dataset, which is the historical data we already had, build models and find patterns, and use the model in the test dataset to evaluate if it fits the data. For example, we have a dataset of three different species of iris and their features of sepal length and sepal width. We can use this dataset to let computers get the correlation model between the features of sepal and species. By observing the sepal width and length, the computer can help us to identify the species if we get a new flower. We may simply find the pattern and classify the species ourselves, but sometimes we may not have the ability to handle data with many features and seemingly no pattern, so machine learning can be helpful.

Machine learning is commonly used in fields of image and video recognition, prediction, and personalized push notification on social media platforms. Companies mainly apply this method to build price strategies by mining historical price data and making customer segments. In the arts and entertainment industry, it is also useful in marketing research. It would be helpful to get insights into who our customers are, why they choose us and how they will be more satisfied and loyal in any organization.

Data in art organizations

Arts organizations already find benefits in using data-driven methods to engage the audience. Around 90% of nonprofits are collecting data but half of them are not sure how to use it according to The State of Data in the Nonprofit Sector report. 85% of respondents in the Nonprofit Trend report say they “use insights from marketing and engagement data to target outreach efforts. Organizations are used to applying data tools to collect customer data and build stronger relationships. It is easy to find the information of ticket buyers such as email, address, what kinds of performances they like, or how much they spend on your organization’s events. And that helps organizations to make customer segments, know preferences, and reach out to them directly. Based on a research of nonprofits, the most common use of audience data is using the contact details to send out newsletters. And less than 40% of the respondents use the audience data to personalize campaigns and inform the process of creating artworks.

Machine learning in audience analysis

Art organizations should analyze data more deeply. With new technologies, organizations can get data that is bigger and multiple forms and find better insights in audience analysis. Machine learning makes it possible to find patterns in disorganized audience behavior data. We will mainly focus on several applications this technology can bring to art organizations, build better relationships, make recommendations and help improve the services and productions. 

Profile your customers for a better relationship

Customer Relationship Management(CRM) is software to manage all the relationships in a company. Arts organizations are benefiting from it by managing all the disparate activities such as ticketing, fundraising, and data reporting, which makes it easier to target the audience. For example, it allows organizations to turn the transaction record to the relationship. You can easily find the first-time buyers and send them emails about the upcoming event to improve their return rates.

Machine learning offers more possibilities for CRM. It can leverage the data from “what” to “why” based on the CRM platform. Besides using the sales data to predict future profits, it is also useful in profiling your audience. Accurate customer segments bring better connections. Integrating the big data and machine learning method into CRM enables more types of data to the platform and plays an important role in understanding audience behaviors. By mining deeply the historical ticketing sales data, it helps to find more patterns in buyers’ habits and predict the probabilities of whether they will return. 

Organizations can use the results for more accurate segmentation and send different newsletters to audiences with different return rates or buying habits, which enables them to make deeper connections and improve their loyalty. For unstructured data and free text fields, It is difficult to extract common points with traditional statistical methods. However, it is possible to set an algorithm to find out any inquiries, complaints, and references for specific shows. It is also useful in fundraising. Donors are giving because they want the organizations to meet their needs. By getting an accurate profile, we can know more about donors’ interests and explore how the organizations can introduce programs to donors and help them to pursue their needs. It helps to cultivate long-term relationships and encourage giving.

The arts industry has already applied it in customer segmentation. Purple Seven is the leading theater and art data analytics company to use big data to help find insights for organizations. By combining an organization’s CRM with external big data of performing art consumption, Purple Seven can identify which bookers have the greatest likelihood of returning, what their audience is interested in, how to reach them, and where to find new audiences. 

Make personalized recommendations

Machine learning’s application in CRM makes customization possible. A personalized recommendation system is a technique to provide recommendations based on historical behavior data. It is a common application by using big data to analyze user behavior, particularly for online streams like YouTube and Netflix. Netflix uses the watch history of other users who have similar tastes to recommend what you may be most interested in watching next. It excels in analyzing audience profiles and pushing personalized products. To maximize the satisfaction of viewers with different preferences, it even cut out several different versions of the trailer and distributed them to different people through the user profile mastered by big data. The benefit is also significant. Netflix Over 75% of its viewer activity is based on personalized recommendations. They earned over 1 billion due to the recommendation system accounting for over 80% of the content streamed on the platform. 

Unlike the entertainment industry, traditional art organizations focus on off-line events. Currently, the personalized recommendation system is used for galleries. There is software using big data and machine learning to predict the artworks’ price for customers and make personalized recommendations, such as Arternal, Artbase, and Artcloud. Although few recommendation systems are widely used in the theater industry, data scientists are making models to gain insights into the artistic preferences of customers and similarities between performances to make personalized recommendations.

Improve content and service

Organizations want to know not only why the audience chooses us, but also their whole experience, and which parts or details raise their interests or make them satisfied. Analyzing audience behaviors in the events can help us achieve this goal. For the online platform, the user’s clicks, length of stay, and comments will be analyzed by algorithms to find out the pattern of which types of plots are popular. The investment of productions is also based on the prediction of the main characters and plots. The millions of visits per day can bring a large number of data samples to the platform to support the analysis and prediction, to provide the audience with more desired works. 

Museums use a similar method in exploring how the visitors interact with the venue. The British Museum uses machine learning to collect data of visitor experience. They can find out how people experience its exhibitions: what routes they take, what they engage with, how many minutes they take at each installation, and which pieces they choose to ignore. By doing this, the museum can get the point of interest of the audience, know what is working and what is not.  It can be more targeted to making the promotion and event and exhibition design in the future. For example, if more people stay a long time during similar topic paintings, it may be interesting to consider an education tour for these artworks. It helps to increase visitation, harness social outcomes and deliver efficiencies.

Theaters can hardly get the audience’s review during the events, but show lovers love to post their feelings on social media. Theater can get a view of what the audience likes and dislikes about the show by analyzing the unstructured comments on the internet.

Conclusion

After introducing the applications, we found that their commonality is to analyze the behavior of the audience, discover the characteristics of the audience, and use this to engage more audiences, build loyalty and expand the impact of the organization. That is why we use data analytics tools. We want to find a measurable way to know our audience, to discover who they are, why they are here and what they want, and broaden it and connect with it in innovative ways. Arts organizations always value audience feedback, but previously we could only do this using common sense, observation, and interviews of individuals. Now machine gives us a chance to get more comprehensive information and discover hidden insights from a large amount of data. With machine learning, it is easier to get a clear profile of the audience and their story with your organizations and help improve the organization and build deeper relationships in a targeted way. A better understanding of your audience is also a better understanding of your organization. For small art organizations, there may be barriers such as being hard to get a big data sample. Although the pattern will be more accurate with more data, it will still be useful to know more about your audience. 

It is the trend in the world to use big data. Art organizations will not miss the chance to be involved in this trend for an efficient way to make connections with audiences. Arts organizations will gradually discover the importance of data and get used to using analytical tools to discover insights and understand the audience.

Resources

Abernethy, Jacob D., Cyrus Anderson, Alex Chojnacki, Chengyu Dai, John Dryden, Eric M. Schwartz, Wenbo Shen, Jonathan C. Stroud, Laura Burdick, Sheng Yang and Daniel T. Zhang. “Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences.” ArXiv abs/1611.05788 (2016): n. Pag.

“Art Gallery Software Market 2021 High Growth Forecast Due to Rising Demand and Future Trends.” ZNews Africa, February 25, 2022. https://znewsafrica.com/space/318680/art-gallery-software-market-2021-high-growth-forecast-due-to-rising-demand-and-future-trends-artbase-art-galleria-art-systems-masterpiece/.

Carlsson, Rebecca. “Big Data and Museums.” MuseumNext, March 30, 2021. https://www.museumnext.com/article/big-data-and-museums/.

Day, Adrienne. “Data-Driven Connections for a Better World (SSIR).” Using Data and Technology to Create World-Changing Connections Between Nonprofits and Their Supporters, 2020. https://ssir.org/articles/entry/data_driven_connections_for_a_better_world.

“How CRM Can Help You Outperform National Arts Industry Revenue Benchmarks.” NAMP, May 15, 2019. https://namp.americansforthearts.org/2019/05/15/how-crm-can-help-you-outperform-national-arts-industry-revenue-benchmarks.

“How Netflix Used Big Data and Analytics to Generate Billions.” Selerity, September 27, 2021. https://seleritysas.com/blog/2019/04/05/how-netflix-used-big-data-and-analytics-to-generate-billions/.

Midura, Danine. “Leveraging Machine Learning Using CRM Data.” Technology Advisors, July 14, 2021. https://www.techadv.com/blog/leveraging-machine-learning-using-crm-data.

“New Audience.” Purple Seven. Accessed March 2, 2022. New Audience. 2022. Ebook. Purple Seven. https://purplesevenanalytics.com/wp-content/uploads/2018/10/New-Audiences_InfoCosts.pdf.

Sauravdeb. “Introduction to Machine Learning: Iris Dataset.” Medium. Medium, February 18, 2022. https://medium.com/@sauravdeb98/introduction-to-machine-learning-iris-dataset-58f2f30f966e.

“The British Museum Is Using Big Data to Help Visitors Learn More about History.” Microsoft News Centre UK, July 4, 2017. https://news.microsoft.com/en-gb/2017/07/04/the-british-museum-is-using-big-data-to-help-visitors-learn-more-about-history/.

“The National Gallery Predicts the Future with Artificial Intelligence.” Digital meets Culture, September 14, 2017. https://www.digitalmeetsculture.net/article/the-national-gallery-predicts-the-future-with-artificial-intelligence/.

“The State of Data in the Nonprofit Sector.” everyaction. Accessed March 2, 2022. https://cdn2.hubspot.net/hubfs/433841/The_State_of_Data_in_The_Nonprofit_Sector.pdf.

“Uses of Machine Learning: List of Top 10 Uses of Machine Learning.” EDUCBA, March 2, 2021. https://www.educba.com/uses-of-machine-learning/.

Villaespesa, Elena. “Digital Culture 2014 – How Arts Organisations Use Audience Data.” arts&metrcs, December 8, 2014. https://www.artsmetrics.com/en/how-arts-organisations-use-audience-data/.

“What Is the Data, Information, Knowledge, Wisdom (DIKW) Pyramid?” Ontotext, October 22, 2020. https://www.ontotext.com/knowledgehub/fundamentals/dikw-pyramid/.

Yu, Allen. “How Netflix Uses AI and Machine Learning.” Medium. Becoming Human: Artificial Intelligence Magazine, October 1, 2019. https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe.

Artificial intelligence in the arts is growing increasingly more complex. It’s 2022, and robots are co-creating art, NFTs are celebrating and confusing art consumers around the world, and at least 85% of Americans have smartphones that give immediate access to endless amounts of streaming content. This article specifically covers the recommendation algorithms built to support content delivery for this majority, their cultural implications, and bias control.

Pervasive Algorithms

New technologies in the crypto realm and metaverse have distracted many from the increasing prevalence of social media and content streaming applications like Spotify, Facebook, YouTube, TikTok, Twitter, etc. So, before delving into the unsolved problems with these services, it is worth reviewing how important they are in our lives through examining consumer data. According to the Pew Research Center, a majority of adults use social media applications at least once a day. The algorithms these applications use to present relevant content to their users vary technically across companies, but one element remains the same between them. The algorithms are constantly improving their modeling capabilities using a combination of content-based, collaboration-based, and knowledge-based methods. Content-based methods use metadata about items presented to recommend what a user would like to see, whereas collaborative and knowledge-based methods use user profiles and behavior and a combination of user and content data, respectively, to share content relevant to specific users. The high-level goal of the algorithm, regardless of its specific structure, is to add personalization and enjoyment to the application for its user.

The Center for Humane Technology calls the cause of our daily reliance on personalized social media and content streaming applications “pervasive technology.” Advanced recommendation algorithms disrupt our daily lives because they constantly reinvent what we see to appeal to our psychological wiring. For example, recommending dramatic or violent social media content appeals to human curiosity, and creating endless scrolling in an application gives users the power to continue engaging in that curiosity consumption without bound. When asked to describe positive experiences with recommendation algorithms, a group of peers responded that they enjoy receiving a mixture of similar and new content, events to attend based on past consumption, and niche accounts that make them feel “extra special and unique.” The common thread in these sentiments is the feeling of being known by the application. Recommendation algorithm tuning has progressed to the point that individuals expect algorithms to understand them, and they reward these systems with their undivided attention when they do.

TikTok currently has one of the best content recommendation algorithms on the market in terms of capturing users’ interests and recommending desired content. It is estimated that 90%-95% of what TikTok users watch on the application comes from the TikTok algorithms’ recommendations. For reference, only approximately 70% of YouTube views can be traced to its recommendation algorithms, and YouTube is widely considered one of the best content streaming platforms with more than 2 billion active users. TikTok is still changing its algorithm often to make even more improvements in content recommendation, but this video provides a general use case for exploring how it curates individualized content.

Cultural Implications – Spotify Case

A major unanswered question surrounding recommendation algorithms and their control over society’s content consumption is: how will these algorithms influence cultural traditions and preferences around art in the long-term? Spotify has the most transparent recommendation algorithm out of most massive content sharing applications, so it is easiest to explain the potential cultural disruptions facilitated by recommendation algorithms using it as an example.

Before platforms like Spotify existed, music was a means of cultural expression. Communities shared songs live from generation to generation to tell stories about their struggles and triumphs, and even in the early 2000s when CDs, mp3 players, and iPods were prevalent, music exposure was dictated largely by the community individuals lived in. However, biases in Spotify’s algorithms are now changing the narrative of how music integrates with society because they expose listeners only to whatever culture(s) they understand.

The first bias that influences what the Spotify algorithm shares is the popularity bias. This bias occurs when a user enters Spotify’s user matrix upon creating an account. The system has little context with which to build successful recommendations upon at this point, so the application is programmed to begin presenting a selection of popular music to the user to kick off their content personalization. The more the user listens, the more Spotify can narrow from a broad list of hits to their most resonant, curated content, creating what has been referred to as an “echo chamber” of listening behavior. Because algorithms are influenced by user behavior, this programming also results in a feedback loop between users and the app that can only be broken if the user searches content outside of the types Spotify presents. There is no way to know exactly how this tunneled call and response of the Spotify user and recommendation algorithm will change our society’s relationship to music, but there have already been urgent calls to redesign music streaming algorithms due to their gender biases. This issue hints at a turbulent relationship between the streaming music industry and equitable music access that has surely changed society’s music affinities already.

Making Algorithms Less Bias

First and foremost, recommendation algorithms are largely built to serve their application’s parent company. The more time users spend on the applications, the more the companies that develop them make. For these platforms to be sustainable and fair to the artists, musicians, and content creators that use them, however, their developers would need to change their goal and code with extreme consciousness toward the biases they feed into them. Because we know that these systems have impactful and unchecked influences on our culture, we must think of their development as a venture in computational social science. An algorithm’s development team can do a lot by bringing interdisciplinary perspectives into the room when building recommendation models to positively or negatively affect the bias in their performance. To mitigate instances of bias in algorithmic design, the developers must first consider diversifying their team.

1. Diversify Development Teams

As Kriti states in “How to keep human bias out of AI”, many women in the computer science field are not respected, trusted, or valued for their capabilities and intelligence as highly as their male colleagues. This issue in the technology field may relate to the fact that gender biases have been discovered in music streaming services. Based on statistics from big technology companies like Google and Facebook, it is likely that pervasive algorithms behind such services are primarily developed by men. Although big tech companies are making great strides to change this, it is also likely that these algorithms are being developed by individuals who identify as White or Asian. 

It takes diverse minds to nurture an algorithm to make diverse decisions. In other words, the more perspectives a development team can engage about how algorithms could potentially leave cultures behind in their data, behaviors, and reach, the less likely it is that the algorithm will be culturally disruptive in a homogenizing manner. Potentially, the diverse team can even craft an algorithm that uplifts diverse and marginalized voices in ways the physical world does not to change our world for the better. Training diverse teams to understand and manage their personal biases could help them grow toward making socially informed and equitable algorithmic decisions as well.

2. Talk about the Data

The complexity of recommendation algorithms is beyond comprehension for most individuals, so it can be difficult to fathom how to improve them from the outside and ensure they fairly represent diverse creators. However, it is widely known that humans create algorithms by feeding them massive amounts of data and fine-tuning their responses to improve the accuracy of their suggestions. This training data that developers choose to give the algorithms matters because the algorithm can only respond within the confines of the information it is given. To provide an oversimplified example for why this is important to artists, imagine that a new music streaming service is building a simple recommendation algorithm. They feed the algorithm a training dataset full of the song metadata and user listening behavior for 200,000 pop music lovers. Then, they recommend a song to a new user who rarely listens to music by collaboratively matching the user with the profiles in this data set. The algorithm will likely recommend a pop song to the new user because it has learned that these songs are popular.

The issue in the above example scenario is that having only pop fans’ data in the algorithm’s training set makes it less likely that a user will get exposure to other types of music that the service provides. For an artist outside the pop genre, this means that they may not gain exposure simply because of this data oversight. It is therefore crucial that developers think about diversity and popularity rankings not just in their physical team, but in the data they feed their algorithms when training them. Over the past few years, lawmakers have proposed over 100 bills in over 20 states aiming to make data sharing more transparent within the AI and recommendation system space and pressure companies to think about data implementation biases.

3. The Power of the User

One of the most exciting elements of recommendation algorithms is that they are always evolving. While it is the onus of the companies that create them to protect artists and content creators by making sure all content has a fair chance of getting presented to users on their platforms, individuals can make up for their blind spots through their consumption behavior. For example, using application search features to find new content outside of the genres typically presented teaches the algorithm to nurture a diverse individual content ecosystem. 

It will ultimately take collaborative efforts beginning at the highest level of technology team recruitment and stepping down to the individual’s consumption behavior to mitigate algorithmic biases. While comprehensive, this work will affect the long-term cultural and artist equity implications of popular recommendation algorithms, thus affecting how our society engages with art in the future.

References

“22 Examples of Artificial Intelligence in Daily Life (2022) | Beebom.” Accessed February 20, 2022. https://beebom.com/examples-of-artificial-intelligence/.

Amazon Science. “The History of Amazon’s Recommendation Algorithm,” November 22, 2019. https://www.amazon.science/the-history-of-amazons-recommendation-algorithm.

AMT Lab @ CMU. “How Streaming Services Use Algorithms.” Accessed February 27, 2022. https://amt-lab.org/blog/2021/8/algorithms-in-streaming-services.

AMT Lab @ CMU. “Streaming Service Algorithms Are Biased, Directly Affecting Content Development.” Accessed February 27, 2022. https://amt-lab.org/blog/2021/11/streaming-service-algorithms-are-biased-and-directly-affect-content-development.

Auxier, Brooke, and Monica Anderson. “Social Media Use in 2021.” Pew Research Center: Internet, Science & Tech (blog), April 7, 2021. https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/.

“Bias in AI: What It Is, Types, Examples & 6 Ways to Fix It in 2022,” September 12, 2020. https://research.aimultiple.com/ai-bias/.

Born, Georgina. “Artificial Intelligence, Music Recommendation, and the Curation of Culture: A White Paper,” n.d., 27.

Borodescu, Ciprian. “The Anatomy of High-Performance Recommender Systems – Part 1 – Algolia Blog.” Algolia Blog. Accessed February 21, 2022. https://www.algolia.com/blog/ai/the-anatomy-of-high-performance-recommender-systems-part-1/.

“DI_CIR-State-of-AI-4th-Edition.Pdf.” Accessed February 20, 2022. https://www2.deloitte.com/content/dam/insights/articles/US144384_CIR-State-of-AI-4th-edition/DI_CIR-State-of-AI-4th-edition.pdf.

Ferraro, Andrés, and Christine Bauer. “Music Recommendation Algorithms Are Unfair to Female Artists, but We Can Change That.” The Conversation. Accessed February 21, 2022. http://theconversation.com/music-recommendation-algorithms-are-unfair-to-female-artists-but-we-can-change-that-158016.

“Guide to Customer Experience Digital Transformation in 2022,” May 31, 2020. https://research.aimultiple.com/cx-dx/.

“How to Keep Human Bias out of AI | Kriti Sharma – YouTube.” Accessed February 24, 2022. https://www.youtube.com/watch?v=BRRNeBKwvNM&t=17s.

Influencive. “The Future of Art: A Recommendation-Engine-Powered, Fully-Customizable, NFT Marketplace,” February 17, 2022. https://www.influencive.com/the-future-of-art-a-recommendation-engine-powered-fully-customizable-nft-marketplace/.

Knibbe, Julie. “Fairness, Diversity & Music Recommendation Algorithms.” Music Tomorrow (blog), September 21, 2021. https://music-tomorrow.com/2021/09/fairness-and-diversity-in-music-recommendation-algorithms/.

NW, 1615 L. St, Suite 800 Washington, and DC 20036 USA202-419-4300 | Main202-857-8562 | Fax202-419-4372 | Media Inquiries. “Demographics of Mobile Device Ownership and Adoption in the United States.” Pew Research Center: Internet, Science & Tech (blog). Accessed February 27, 2022. https://www.pewresearch.org/internet/fact-sheet/mobile/.

Pastukhov, Dmitry. “Inside Spotify’s Recommender System: A Complete Guide to Spotify Recommendation Algorithms.” Music Tomorrow (blog), February 9, 2022. https://music-tomorrow.com/2022/02/how-spotify-recommendation-system-works-a-complete-guide-2022/.

“Persuasive Technology.” Accessed February 20, 2022. https://www.humanetech.com/youth/persuasive-technology.

“Programmers, Lawmakers Want A.I. to Eliminate Bias, Not Promote It.” Accessed February 27, 2022. https://pew.org/3v1vGYh.

Schwartz Reisman Institute. “Algorithms in Art and Culture: New Publication Explores Music in the Age of AI.” Accessed February 21, 2022. https://srinstitute.utoronto.ca/news/ai-music-recommendation-and-the-curation-of-culture.

Social Media Marketing & Management Dashboard. “How Does the YouTube Algorithm Work in 2021? The Complete Guide,” June 21, 2021. https://blog.hootsuite.com/how-the-youtube-algorithm-works/.

Teichmann, Jan. “How to Build a Recommendation Engine Quick and Simple.” Medium, August 6, 2020. https://towardsdatascience.com/how-to-build-a-recommendation-engine-quick-and-simple-aec8c71a823e.

The Social Dilemma. “The Social Dilemma – A Netflix Original Documentary.” Accessed February 27, 2022. https://www.thesocialdilemma.com/.

TrustRadius Blog. “2020 People of Color in Tech Report,” September 21, 2020. https://www.trustradius.com/vendor-blog/people-of-color-in-tech-report.

Wall Street Journal. How TikTok’s Algorithm Figures You Out | WSJ, 2021. https://www.youtube.com/watch?v=nfczi2cI6Cs.

“Workforce Representation – Google Diversity Equity & Inclusion.” Accessed February 27, 2022. https://diversity.google/annual-report/representation/.

Writer, Senior. “How Top Tech Companies Are Addressing Diversity and Inclusion.” CIO (blog). Accessed February 27, 2022. https://www.cio.com/article/193856/how-top-tech-companies-are-addressing-diversity-and-inclusion.html.

“YouTube User Statistics 2022 | Global Media Insight.” Accessed March 1, 2022. https://www.globalmediainsight.com/blog/youtube-users-statistics/.

Dalí Lives is an advanced instance of a growing phenomenon with the potential to disrupt both the art world and technologically mediated inter personal relationships. Ghostbots (sometimes called Griefbots) are an impending challenge for post mortem privacy and the arts space, and have found exploration in some recent socially conscious science fiction. This technology is defined loosely as efforts to use digital versions of an individual to recreate them in a way that can survive after the death of the individual. The first of these to gain major traction, Eternime, is now defunct after a period of media hype:

Two successors in the space, HereAfter AI and Replika AI, were both founded by computer scientists as projects to recreate specific individuals that they were hoping to memorialize. The ultimate fates of each of these projects is telling as to the future of this space. Replika does not recreate individuals, but rather uses input from individuals to create a digital friend. HereAfter AI is essentially a digital diary. For something like Dalí Lives to become mainstream for artists and for people seeking a form of digital immortality, something in between HereAfter and Replika needs to become widely available. HereAfter seeks to recreate an individual, while Replika applies machine learning to create generative conversations that an individual will respond well to. There is no bridge between this at the moment that is available without the resources of an entire museum. This has wide implications for who has the right to a digital immortality that is similar to the gate kept nature of most advanced AI – at the moment it is limited to those rich enough to hire a team of computer scientists and pay for the computing power, or to people who are already famous enough that organizations are willing to put in the effort and time to recreate them.

The consequences of poorly executed generative AI are vast – as it could lead to an individual or artist producing nonsensical or harmful conversations during an artistic expression or during conversations with close relatives. In scenarios where the artist or individual is deceased, there is no recourse if an AI is not representative of who they are, and there is a possible future to this technology where the quality of your digital afterlife is mediated by wealth. This is already present, as HereAfter AI and Mind Bank AI (discussed later) are very expensive. HereAfter AI relies on a paid, tier subscription model to maintain a digital afterlife. Like a Medieval Indulgence.

There is one other mass market Ghostbot/Griefbot that I found, Mind Bank AI, which markets itself as a “Digital Twin”. Mind Bank AI is a pretty shady company. Like HereAfter, I am unconvinced that their privacy practices are ready to handle the implications of a technology explicitly designed to gather the most intimate stories of a person’s life.

Mind Bank AI plays a slight of hand. It does not yet seem to use generative conversations. Instead, it draws people in with vague promises of self help and an eventual digital twin once AI technology has reached the point where it is possible. However, Dalí Lives proves that it is already largely possible, it is just gate kept by wealth and access to enough data about a person.

Mind Bank AI and Eternime are/were designed to be used for extended periods, possibly years, to gather enough data to recreate an individual. This is a reason why this technology is particularly suited to disrupt the arts space, as artists are likely to have vast banks of carefully crafted and stored records of their work, public appearances, and thought processes. Ghostbots, Griefbots, and Digital Twins are in very nascent stages, and are something to be watched along with privacy laws and the increased access to AI to people without vast resources and technology knowledge.

Bibliography

A Grief Bot Could “Resurrect” My Dead Husband. No Thanks. | HuffPost Life. (n.d.). Retrieved February 25, 2022, from https://www.huffpost.com/entry/grief-bots-death-loved-ones_n_5ad5193fe4b016a07e9f9a2d

Ahmad, M. A. (2016). After death: Big data and the promise of resurrection by proxy. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 397–408.

A Son’s Race to Give His Dying Father Artificial Immortality | WIRED. (n.d.). Retrieved February 25, 2022, from https://www.wired.com/story/a-sons-race-to-give-his-dying-father-artificial-immortality/

Eternime. (n.d.). Retrieved February 25, 2022, from https://web.archive.org/web/20180317190713/http://eterni.me/

Eternime. (2018, March 17). https://web.archive.org/web/20180317190713/http://eterni.me/

Eternime Alpha Two on Vimeo. (n.d.). Retrieved February 25, 2022, from https://vimeo.com/142260863

Eterni.me—Become Virtually Immortal. (2014, January 30). https://web.archive.org/web/20140130073423/http://eterni.me/

Figueroa, M. (2022, January 18). Ghostbots, the Quest for Digital Immortality and the Law—JURIST – Commentary—Legal News & Commentary. https://www.jurist.org/commentary/2022/01/mauricio-figueroa-ghostbots-digital-immortality-law/

HereAfter AI — Save the Stories of Someone You Love. (n.d.). Retrieved February 25, 2022, from https://www.hereafter.ai/

‘Hey, Google! Let me talk to my departed father.’ (n.d.). Washington Post. Retrieved February 25, 2022, from https://www.washingtonpost.com/technology/2019/08/29/hey-google-let-me-talk-my-departed-father/

LifeNaut. (n.d.). LifeNaut. Retrieved February 25, 2022, from https://www.lifenaut.com/

Replika. (n.d.). Retrieved February 25, 2022, from https://replika.ai/

Replika—Virtual AI Friend on the App Store. (n.d.). Retrieved February 25, 2022, from https://apps.apple.com/us/app/replika-your-ai-friend/id1158555867

Roman Mazurenko on the App Store. (n.d.). Retrieved February 25, 2022, from https://apps.apple.com/us/app/roman-mazurenko/id958946383

Selinger, E. (2018, March 13). Can Bots Help Us Deal with Grief? Medium. https://medium.com/s/when-robots-rule-the-world/can-bots-help-us-deal-with-grief-3de488cae96

Speak, Memory. (n.d.). Retrieved February 25, 2022, from https://www.theverge.com/a/luka-artificial-intelligence-memorial-roman-mazurenko-bot

Tech company lets you chat with your dead loved ones | Metro News. (n.d.). Retrieved February 25, 2022, from https://metro.co.uk/2021/12/16/ai-company-lets-you-chat-with-your-dead-loved-ones-15781759/

The Griefbot That Could Change How We Mourn. (n.d.). Retrieved February 25, 2022, from https://www.thedailybeast.com/the-griefbot-that-could-change-how-we-mourn

The Journey to Digital Immortality | by Marius Ursache | Medium. (n.d.). Retrieved February 25, 2022, from https://medium.com/@mariusursache/the-journey-to-digital-immortality-33fcbd79949

Eterni.me links to this post from their now defunct url. It is 7 years old and seems to be the only remnant of the company left. I think it would be noteworthy to do a “where are they now” of the founders.

Watch Black Mirror | Netflix Official Site. (n.d.). Retrieved February 25, 2022, from https://www.netflix.com/title/70264888

When his father died, this technologist created a chatbot, so his kids could talk to their grandfather | CBC Radio. (n.d.). Retrieved February 25, 2022, from https://www.cbc.ca/radio/spark/386-attention-residue-new-design-ethics-and-more-1.4548894/when-his-father-died-this-technologist-created-a-chatbot-so-his-kids-could-talk-to-their-grandfather-1.4548924