Project 11

just my lil German boy

sketch
// Sowang Kundeling Section C Project 11

var backgroundX = 355;
var foregroundX = 250;

function preload() {
    germanboy = loadImage("https://i.imgur.com/8g7kXHX.png");
    neighborhood = loadImage("https://i.imgur.com/ueqfD0Q.png");
    house = loadImage("https://i.imgur.com/VeVnWaO.png");
    montain = loadImage("https://i.imgur.com/xImWYKs.png");

    long = loadImage("https://i.imgur.com/6kAoEti.png");
    short = loadImage("https://i.imgur.com/hGhArWq.png");
}


 function setup() {
    createCanvas(480, 240);
 }


 function draw() {
// sky
    noStroke();
    fill(180, 227, 242);
    rect(0, 0, 480, 25); // x7
    fill(163, 194, 207);
    rect(0, 25, 480, 25);
    fill(137, 165, 176);
    rect(0, 50, 480, 25);
    fill(121, 146, 156);
    rect(0, 75, 480, 25);
    fill(98, 119, 128);
    rect(0, 100, 480, 25);
    fill(79, 95, 102);
    rect(0, 125, 480, 25);
    fill(60, 74, 79);
    rect(0, 150, 480, 25);
    
    imageMode(CENTER);

// background elements
    for (var i = 0; i < 2; i ++) {
        image(short, backgroundX - (i * 150), 159, short.width/2, short.height/2);
        image(long, (backgroundX + 90) - (i * 200), 145, long.width/2, long.height/2);
    }

// land
    fill(107, 96, 81);
    rect(0, 175, 480, 65); // land

// foreground elements
    image(montain, foregroundX-120, 115, 265, montain.width/4, montain.height/4);
    image(neighborhood, foregroundX - 140, 137, neighborhood.width/2, neighborhood.height/2);
    image(house, foregroundX + 250, 140, house.width/4, house.height/4);
    image(germanboy, 100, 200, germanboy.width/2, germanboy.height/2);
    backgroundX -= 1; 
    foregroundX -= 1;

// reset
    if (backgroundX <= -300) {
        backgroundX = 900;
    }

    if (foregroundX <= -300) {
        foregroundX = 825
    }
}

Looking Outwards 11

In the fields of CG and AI, unintentional bias exists in virtual human portraits. The author believes that computer programmers and artists must think thoroughly about every detail in their design process to diminish any potential harm to a specific group of people. An example the author provided in the article is the glowing effect that was virtually generated on portraits by Johannes Vermeer and other contemporary artworks. Though the glow effect works effectively on white skin, it is not a dominant quality of black skin, which automatically creates a difference in presentations. The solution to this issue is based upon the artists’ realization of creating a diverse range of options for these qualities and refining those options to match reality accurately.

Gaskins, Nettrice. “Ai & Creativity: Addressing Racial Bias in Computer Graphics.” Medium, Medium, 11 Aug. 2021, https://nettricegaskins.medium.com/ai-creativity-addressing-racial-bias-in-computer-graphics-f5fc0c255e7.

Johannes Vermeer. “Girl With a Pearl Earring (detail),” dated c. 1665
Google search results for “skin shaders”

anabelle’s blog 11

The article Beeple’s digital ‘artwork’ sold for more than any painting by Titian or Raphael,” discusses what the impacts the capitalization of art may have on the industry through the lens of NFTs. It highlights the auctioning of Beeple’s Everydays: The First 5000 Days for $69.3 million, one of the highest bids for art in history. The author criticizes how nonphysical, nontechnical, and nonbeautiful art has gripped the capitalist market by rebranding them as special NFTs, or digital property. He seems most turned off by how the two highest bidders were both founders of blockchain companies — blockchains are exactly how NFTs receive their value, so the entire event feels orchestrated in his opinion. I think NFTs can be respected as a type of commerce, but I hesitate to call each individual work art. Either way, new technology and new ways of creating goods for the market will always appear and resisting them can be pretty futile.

Citation:
Washington Post. “Perspective | Beeple’s Digital ‘Artwork’ Sold for More than Any Painting by Titian or Raphael. But as Art, It’s a Great Big Zero.” Accessed November 19, 2022. https://www.washingtonpost.com/entertainment/museums/beeple-digital-artwork-sale-perspective/2021/03/15/6afc1540-8369-11eb-81db-b02f0398f49a_story.html.

Looking Outward 11

link: https://ars.electronica.art/aeblog/en/2020/04/10/women-in-media-arts-ai/

I look into the article “Women in Media Arts: Does AI think like a (white) man?”, where the author discusses the inherent discrimination in artificial intelligence due to the predominantly white male society. This is due to the fact that “the AI is only as good …  as the data it feeds.” Thus if the internet is still populated by artwork from white males or drawn from a white male perspective, this inherent bias will forever exist in the AI’s algorithm.

This statement has been exemplified by an AI face recognition software, where the error rate is significantly high among women of color, displaying this inherent prejudice in the AI’s database. AI teams that lack diversity in gender and race would most definitely produce software that lacks a bigger perspective, and thus bares bias that favors white males.

The article has given examples of efforts to counteract this bias, such as the feminist data set, an ongoing multi-year project that collects feminist data such as artworks, essays, interviews, and books on feminism or from that a feminist’s perspective. This data set would help push diversity in the AI algorithm and broadens the perspective of the AI’s database.

Looking Outwards 11

Gender Shades

https://ars.electronica.art/aeblog/en/2020/04/10/women-in-media-arts-ai/

As artificial intelligence software that detects, recognizes, and classifies faces becomes increasingly popular, researchers Joy Buolamwini and Timnit Gebru are examining how codified biases in facial recognition software often misgender people who are not white or even fail to recognize their faces completely in their project titled “Gender Shades”. These biased facial recognition softwares are often created by male-dominated teams of computer scientists who lack diversity in ethnicity, race, and gender. Additionally, the data sets that these computer scientists feed their programs also often lack diversity, which is why the software does a poor job of recognizing people who are not white or male. To combat this, Buolamwini and Gebru have created a new standard of data set taken from a diverse group of 1270 parliamentarians from Africa and Europe. This new benchmark dataset for gender and racial diversity will help facial recognition softwares learn to recognize all faces and distinguish between genders and ethnicities without bias.

Project 11 Landscape

project11

It was fun to figure out how to create a whole landscape without having to define absolutely everything and making it more interesting with randomness. I added a shooting star with a very small creation probability to create a little surprise within the landscape every once in a while.

// Rachel Legg / rlegg / Section C

//have trees and stars move by
//every once in a while, a surprising shooting star
//car in the middle (moving past trees)

var stars = [];
var trees = [];
var shootingStars = [];

var car;

function preload(){
    car = loadImage("https://i.imgur.com/C7SXV3W.png");
}

function setup() {
    createCanvas(450, 300);
    frameRate(10);

    //initial stars and trees
    for(var i = 0; i < 6; i++){
        var sx = random(width);
        stars[i] = makeStar(sx);
    }
    for(var i = 0; i < 10; i++){
        var sx = random(width);
        trees[i] = makeTree(sx);
    }
    //random shooting star
    for(var i = 0; i < 1; i++){
        var sx = random(width);
        shootingStars[i] = makeShootingStar(sx);
    }

}

function draw() {
    //sky - dark blue
    background(10, 52, 99);
    //ground
    fill("darkgreen");
    noStroke();
    rect(0, 240, 500, 75);
    //road
    fill("gray");
    rect(0, 260, 495, 10)

    updateAndDisplayStar();
    removeStarOutOfView();
    addNewStar();

    updateAndDisplayShootingStars();
    removeShootingStarOutOfView();
    addNewShootingStars();

    updateAndDisplayTree();
    removeTreeOutOfView();
    addNewTree();

    //car stays stationary as scenery moves by
    //drew out car in illustrator & uploaded to imgur
    image(car, width/2 - 60, 207, 165, 65);

}

//stars object
function makeStar(starLocationX){
    var star = {x : starLocationX,
                y : random(0, 170),
                starScale : random(0.1, 0.5),
                speed : -1,
                move : moveStar,
                display : displayStar}
        return star;
}

function displayStar(){
    push();
    translate(this.x, this.y);
    fill(255);
    noStroke();
    scale(this.starScale);
    beginShape();
    vertex(50, 18);
    vertex(61, 37);
    vertex(83, 43);
    vertex(69, 60);
    vertex(71, 82);
    vertex(50, 73);
    vertex(29, 82);
    vertex(31, 60);
    vertex(17, 43);
    vertex(39, 37);
    endShape();
    pop();
}

//update star positions and display
function updateAndDisplayStar(){
    for(var i = 0; i < stars.length; i++){
        stars[i].move();
        stars[i].display();
    }
}

function addNewStar(){
    var newStarProbability = 0.01;
    if (random(0, 1) < newStarProbability){
        stars.push(makeStar(width));
    }
}

function removeStarOutOfView (){
    //if star goes off left edge, remove from array
    var starsToKeep = [];
    for (var i = 0; i < stars.length; i++){
        if (stars[i].x + 50 > 0) {
            starsToKeep.push(stars[i]);
        }
    }
    //stars left
    stars = starsToKeep;
}

function moveStar(){
    this.x += this.speed;
}


//trees object
function makeTree(treeLocationX){
    var tree = {xt : treeLocationX,
                yt : 240,
                treeScale : random(0.1, 0.5),
                treeColor : color(0, random(75, 255), 0),
                speedT : -3,
                moveT : moveTree,
                displayT : displayTree}
    return tree;
}

function displayTree(){
    push();
    translate(this.xt + 60, this.yt); //add 60 so transitions onto screen
    fill(255);
    noStroke();
    scale(this.treeScale);
    
    //tree!
    noStroke();
    fill("brown");
    rect(0, 0, 10, -50);
    rect(1, 0, -10, -50);
    fill(this.treeColor);
    triangle(-60, -50, 60, -50, 0, -400);

    pop();
}

//update tree positions and display
function updateAndDisplayTree(){
    for(var i = 0; i < trees.length; i++){
        trees[i].moveT();
        trees[i].displayT();
    }
}

function addNewTree(){
    var newTreeProbability = 0.04;
    if (random(0, 1) < newTreeProbability){
        trees.push(makeTree(width));
    }
}

function removeTreeOutOfView (){
    //if star goes off left edge, remove from array
    var treesToKeep = [];
    for (var i = 0; i < trees.length; i++){
        if (trees[i].xt + 100 > 0) {
            treesToKeep.push(trees[i]);
        }
    }
    //trees left
    trees = treesToKeep;
}

function moveTree(){
    this.xt += this.speedT;
}



//shooting star object
function makeShootingStar(locationX){
    var shootingStar = {xs : locationX,
                ys : random(75, 125),
                shootingStarScale : random(0.1, 0.3),
                speedS : -8,
                moveS : moveShootingStar,
                displayS : displayShootingStar}
    return shootingStar;
}

function displayShootingStar(){
    push();
    translate(this.xs + 60, this.ys); //add 60 so transitions onto screen
    fill("yellow");
    noStroke();
    scale(this.shootingStarScale);
    
    //shooting star
    beginShape();
    vertex(50, 18);
    vertex(61, 37);
    vertex(83, 43);
    vertex(69, 60);
    vertex(71, 82);
    vertex(50, 73);
    vertex(29, 82);
    vertex(31, 60);
    vertex(17, 43);
    vertex(39, 37);
    endShape();

    stroke("yellow");
    strokeWeight(2);
    line(60, 37, 100, 37);
    line(60, 47, 110, 47);
    line(60, 57, 120, 57);

    pop();
}

//update tree positions and display
function updateAndDisplayShootingStars(){
    for(var i = 0; i < shootingStars.length; i++){
        shootingStars[i].moveS();
        shootingStars[i].displayS();
    }
}

function addNewShootingStars(){
    var newShootingStarsProbability = 0.008;
    if (random(0, 1) < newShootingStarsProbability){
        shootingStars.push(makeShootingStar(width));
    }
}

function removeShootingStarOutOfView (){
    //if star goes off left edge, remove from array
    var shootingStarsToKeep = [];
    for (var i = 0; i < shootingStars.length; i++){
        if (shootingStars[i].xs + 100 > 0) {
            shootingStarsToKeep.push(shootingStars[i]);
        }
    }
    //trees left
    shootingStars = shootingStarsToKeep;
}

function moveShootingStar(){
    this.xs += this.speedS;
}



LookingOutwards 11

In her article, Sohpie Davies sheds light on the increase of the “digital divide” during COVID-19 through two paintings; one of a woman reading an iPad and another of children dreaming of computers. The “digital gap” is the gap in accessibility to computers and the internet. Davies explains that during the pandemic when the use of the internet increased worldwide due to quarantine but only in the more-developed countries. While the internet allowed many people to sustain incomes and keep socially connected, it also needs to be said that computers and the internet can be as inclusive as they are exclusive. People who cannot access these technologies are quickly left behind, especially during COVID-19 when so much of the world went online for economic and social value. These people are minorities, often poor, and often in the least-developed countries. One such group is women. Worldwide, women are less likely to have and connect to technology because of gender inequality, and thus, in the workplace, which is swiftly becoming dominated by the internet, women are left with jobs of lesser value as they never learned skills to manage technology. The second painting Davis brings up explains how children during the pandemic were academically left behind because they could not afford the technology that would allow them to learn remotes. Both of these groups, due to discrimination or economic disadvantage are left behind in a world that is quickly moving past them. 

“Spanish art show spotlights ‘hidden’ digital divide in pandemic” by Sophie Davies

Vase depicting children dreaming of computers.

Gender and Racial Bias in AI

A societal issue I read about was how Artificial Intelligence has a problem with gender and racial bias. The article also offers solutions in how to fix that. The article was written by Joy Buolamwini for Time magazine in 2019. Buolamwini is a computer scientist and poet of code who uses art and research to illuminate the social impacts of artificial intelligence. She has founded the Algorithmic Justice League, a foundation working towards creating more equitable and accountable technology in the world. The article itself is about her MIT Thesis, in which she used AI services from Microsoft, IBM, and Amazon, and used photos of famous black women to see how they AI would identify them. When she evaluated them, she found that for darker-skinned women, errors of guessing the gender of the faces were 35%, and failed to correctly classify the faces of Oprah Winfrey, Michelle Obama, and Serena Williams. She found that by using a white mask, the computer then was able to correctly identify the gender.


The issue surrounding gender and racial bias in AI, is due to the fact that women of color rarely are in positions to develop this technology. Most of the technology in AI fields are created by white males. The AI’s data set that it is using to help recognize faces also has less women of color than men or lighter skinned people. Buolamwini highlights the many different organizations that are trying to combat this bias in AI, and calls for governments and police to stop using this technology in identifying individuals, as it will incorrectly identify women of color and perpetuate a system of abuse.

How I’m Fighting Bias in Algorithms TED talk by Joy Buolamwini from March 2017

Blog 11

NFTs have created vast spaces in which artists can get well deserved and long overdue compensation for their labor. Historically, digital art and media has been free for use on the Internet because of the high shareability and the ability to just copy and paste. NFTs encourage artists to be more creative as there is a space for reward and the Internet is limitless. However, NFTs do have a lot of societal and environmental downsides. Some works get plagiarized and sold, and copyright laws are blurred in the new space. NFTs also require a lot of compusing power and server farms are powered by fossil fuels. While there may be an assumption that online art is more environmentally friendly, it is actually quite the opposite.

https://time.com/5947720/nft-art/

Looking Outwards – 11

Impressions from women in media art

In Anna Gruaber’s “Women in Media Arts: Does AI think like a (white) man?” Gruaber features different artist/activists and their projects related to AI and feminism. Artificial intelligence is becoming a very important topic in regards to diversity and ethics. Activists have started pointing out problematic prejudices and distortions of supposedly objective algorithms. While there is a low proportion of women in IT professions, the real problem are the biased data sets used in AI and Machine learning.

Joy Buolamwini and Timnit Gebru are activists who investigate the prejudice of AI recognition systems. In this project, “Gender Shades,” the error rate is significantly higher among women; especially those with darker skin. Information about skin color is extremely important in the context of medical applications. 

Mary Flanagan’s project “help me know the truth,” shows that a discriminating algorithm does not come from the sexist or racist nature of the machine, but from the systemically racist structure of our society. “Help me know the truth” creates a perfect stereotype from a digital self portrait based on findings of cognitive neuroscience. 

Caroline Sinders wants to counterset bias. Her project, “Feminist Data Set,” is a multi-year art project that combines lectures and workshops to create interventions in the field of machine learning. Sinders wants to collect feminist data through them. The feminist data inclides artworks, essays, interviews, and books on feminism. The data attempts to introduce data collection as a feminist practice.

https://ars.electronica.art/aeblog/en/2020/04/10/women-in-media-arts-ai/