To put it simply, nonfungible tokens (NFTs), are assets one can buy on the blockchain. In the arts administration space, the debate about the role of NFTs in our scope is alive and well. On one hand, there is the argument that NFTs are a new frontier for entrepreneurial artists because they allow for a decentralized avenue for art marketing and sales. The blockchain, in theory, is an extremely well structured platform for artists’ sales because it is managed by smart contracts, which allow artists, if savvy enough, to receive royalties from all future sales. Smart contracts are simply lines of code that program currency and legal transfers in relation to each NFT sale. They elegantly ensure integrity for artists’ rewards by removing any middlemen between them and what they deserve in exchange for their work. 

It is shockingly easy to “mint” an NFT, and by Web3 dictionary standards, minting means converting a digital asset, like a unique drawing, into a marketable item on the blockchain. This is another reason why we are hearing about NFTs in the arts sector. Popular NFT marketplaces like OpenSea use what’s called a “lazy minting” process that lets artists drop their digital goods off for purchase on their platform at no cost. OpenSea then puts the cost of their service on the NFT buyer, charging them an additional 2.5% of the NFT’s market price. This is similar to what a small business may do to their customers using credit cards when they charge an extra processing fee. 

As seen in the image above, NFT trading volume on OpenSea rose from the winter of 2021 to the beginning of 2022, peaked in January of 2022, and began to decline into the rest of the year, while the public’s interest in NFTs followed a similar trajectory. If the NFT market is structured to benefit artists, one could argue that this is a prime time for more artists to improve their NFT-selling strategies and increase this volume. However, there are two sides to every story, and the NFT market is not as easy to navigate in practice as in theory. There are many complexities involved in building a successful NFT brand. For example, setting up a strong smart contract involves collaboration between an artist, a development team, and ideally a lawyer. Lawyers can be crucial in advising smart contract line items for artists like the ability to sell their NFTs off of the blockchain and receive rewards in perpetuity for future sales. Essentially, digital artists have a lot to learn before entering the NFT market, and it will take a plethora of resources for them to create sustainable and smart personas on the blockchain. 

The promise of a decentralized marketplace and autonomy over commercializing art has drawn over 10,000 collections of NFTs to OpenSea. This has created a massive NFT supply, yet not all of the artists who lazy mint on the platform are well equipped with the skills to stand out in the market. This brings up a couple of questions worth exploring. What types of artists can and do succeed in the NFT market in 2022? What type of person is buying NFTs in the current market, and why? As it turns out, the average popular NFT consumer is paying a high price for this digital asset, and they are buying because of factors beyond the NFT’s art itself. A look into OpenSea’s current top 50 most successful NFT art collections will illuminate this. 

Successful NFT Collections – OpenSea Captains

To understand what a “successful” NFT collection looks like on OpenSea, we can examine which collections have earned the highest “volume”, or largest sum, of cryptocurrency to date in Ethereum (ETH.) As of the spring of 2022, the statistics for the top 50 NFT collection sales are as follows. This data was scraped from OpenSea’s Developer API, and the below averages are per collection out of the top 50.

These values show that the range NFT prices in successful OpenSea collections is vast, but that ultimately, popular purchases are concentrated in the multi-thousand-dollar range. Since we have defined successful NFT collections here in terms of their accumulated wealth, we can say that the wealthy portion of the NFT market is making their gains through contributions from individuals who can afford to spend large sums on each individual NFT. One of the most expensive NFTs ever sold was Everydays: The First 5000 Days by the artist Beeple at 69.3 million dollars. So, there are collectors in the market who are so interested that they will even spend millions. From looking further into OpenSea’s popular collections, it is apparent that they have anywhere from 52 to 212,072 individual owners contributing to their sales. This range shares that there are hundreds of thousands of individuals who want a piece of the NFT market as well. For artists’ sake, arts managers should understand this type of collector, who they are, and why they buy.

As one high-value NFT owner stated, his NFT is a status-symbol and one of his most prized possessions. Some large companies like Adidas and Taco Bell have started casually experimenting with NFT purchases to convert them to physical artwork and Web3 real-estate as well. Another reason that big companies and individuals alike invest in NFTs is that these digital items come with extra benefits beyond the ownership of art on the blockchain. In fact, it’s debatable as to whether artistic value is even of consideration for buyers. A dive into NFT benefits and gamification can explain this further. 

NFT Benefit Models to Watch

Play-To-Earn

Video-gamers can now sell NFTs in virtual universes and make actual money, hence there is an incentive to play games with blockchain components to make a tangible profit. Gone are the days of art for art appreciation’s sake with NFTs when this new “play-to-earn” gaming model is readily available. However, there are some positive outcomes of the advent of the play-to-earn model. For example, trading NFTs is a major component of playing games on the blockchain. This means that smart contracts behind the scenes in the games give participants partial ownership of the game, and in some rare cases, a say in its future development. 

Axie Infinity is widely known as one of the most popular play-to-earn games on the market, and it refers to itself as a “player-owned economy” because it is taking revolutionary steps to give control over its future development to its players. The game has a public, multi-year development roadmap for how they plan to incorporate players’ interests into game development. Their desired communal structure for player power is described below. From sales within the game’s NFT marketplace, players can earn “AXS” currency, of which a portion gets added to the game’s “Community Treasury.” The amount of AXS a player accumulates in the Treasury dictates their stake in future development decisions and ultimately provides them with additional rewards. In theory, this structure creates a democratic and meritocratic culture within the Axie Infinity Game Universe that entices users to play by giving them the chance at voting power over its future.

The Avatar Movement

Imagine getting to start from scratch again when creating your digital identity and online presence. It has been decades now since we have been able to do this, and the thought sounds exciting and foreign given the seemed permanence of everything we’ve posted online. However, the Avatar Movement uses “algorithmically generated” NFTs to give buyers a chance at a unique identity in the digital world’s next universe: the metaverse.

The digital artist of a specific avatar collection can create a list of possible features for persona NFTs, give them empowering descriptions, and then combine them to create unique characters that give buyers the chance to adopt a new online identity. Sotheby’s Auction House hosted an NFT avatar collection auction called Queens+Kings in 2021, and some of the avatars from this event are now on OpenSea where collectors can re-mint and rearrange them to create new identities at each sale. Beyond providing creative metaverse identities, NFT avatars are also appealing assets because they can generate passive income for their human owners in the metaverse. They can even have their own agents like some celebrity avatars have had for years now. If the metaverse grows in popularity, the Avatar Movement should grow in tandem as the public joins the bandwagon in making new identities for themselves in the digital frontier.

Exclusivity

Another draw of buying NFTs in the current market is that they typically come with an invitation into an exclusive community. For example, Sotheby’s first NFT auction sold avatars that mimicked players from the Liverpool FC  team, and in return for their purchase gave collectors “access to a members-only LFC Discord community chat channel, virtual hang-outs, competitions, guest appearances, updates from the LFC Foundation, and LFC retail discounts.” Luxury brands are also taking advantage of NFT collections as alternative revenue streams. Louis Vuitton gamified their company’s history by creating an environment where shoppers can learn about the brand’s origins while receiving luxury NFT drops as random rewards. According to crypto.news, “blockchain-based gaming will completely eclipse the entire gaming industry.” It seems as well that block-chain-based NFT sales and gamification will disrupt industries from football to fashion by creating new exclusive benefits for fans.

Miscellaneous

Time-Sensitive and Outcome-Based NFTs

Time-sensitive and outcome-based NFTs – Artists can program NFT purchases to host an element of surprise by revealing their art based on a time or event like the result of the 2020 election.  Coachella 2022 decided to go with a flower theme for their ticket-holder NFTs, and these items were programmed to appear as seeds until the festival began and they grew into different types of flowers, each denoting a unique exclusive deal.

Raffles/Other Surprise Elements

The BitCoin Angel project did well in the NFT market partially because it sold smaller pieces from artist Trevor Jones for a chance at 1/1 ownership of one of his original works. This project turned Jones from a struggling artist into a multimillionaire in a single day.

Takeaways for the Arts Community

One beautiful aspect of the arts is that they prevail through, and react to, the changing world around them. The NFT market and the metaverse space is the next economic frontier for art to infiltrate and revolutionize, but this market is both promising and elitist. Therefore, it’s worth paying attention to as artists and managers alike. To stay abridged to the market, it will be important that the arts and culture sector monitor its trends.

With the rise in play-to-earn gaming and metaverse developments, now is an apt time for artists and arts managers to research and educate each other on minting, smart contracts, and collection gamification best practices. Before entering the market, it is also vital to research both prominent NFT development teams and legal resources to plan for a value-centric and secure benefit strategy and launch. There have been many copyright issues around NFT sales and confusion over what a buyer can do with their NFT once they purchase it. Each of these issues warrants its own in-depth study, and the examples of successful NFT collections above are only the tip of the iceberg in cases for the NFT market’s potential moving forward. This NFT phenomenon may be about more than just buying and selling digital art, but it is surely a robust and decentralized outlet for artists, nonetheless. The NFT market is a playground, colorful and treacherous, for entrepreneurial artists and innovative arts institutions. 

Sources

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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.

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