The artist Stephanie Dinkins has long been a pioneer in combining art and technology in her Brooklyn-based practice. In May she was awarded $100,000 by the Guggenheim Museum for her groundbreaking innovations, including an ongoing series of interviews with Bina48, a humanoid robot.
For the past seven years, she has experimented with artificial intelligences ability to realistically depict Black women, smiling and crying, using a variety of word prompts. The first results were lackluster if not alarming: Her algorithm produced a pink-shaded humanoid shrouded by a black cloak.
I expected something with a little more semblance of Black womanhood, she said. And although the technology has improved since her first experiments, Dinkins found herself using runaround terms in the text prompts to help the AI image generators achieve her desired image, to give the machine a chance to give me what I wanted. But whether she uses the term African American woman or Black woman, machine distortions that mangle facial features and hair textures occur at high rates.
Improvements obscure some of the deeper questions we should be asking about discrimination, Dinkins said. The artist, who is Black, added, The biases are embedded deep in these systems, so it becomes ingrained and automatic. If Im working within a system that uses algorithmic ecosystems, then I want that system to know who Black people are in nuanced ways, so that we can feel better supported.
She is not alone in asking tough questions about the troubling relationship between AI and race. Many Black artists are finding evidence of racial bias in AI, both in the large data sets that teach machines how to generate images and in the underlying programs that run the algorithms. In some cases, AI technologies seem to ignore or distort artists text prompts, affecting how Black people are depicted in images, and in others, they seem to stereotype or censor Black history and culture.
Discussion of racial bias within AI has surged in recent years, with studies showing that facial recognition technologies and digital assistants have trouble identifying the images and speech patterns of nonwhite people. The studies raised broader questions of fairness and bias.
Major companies behind AI image generators including OpenAI, Stability AI and Midjourney have pledged to improve their tools. Bias is an important, industrywide problem, Alex Beck, a spokeswoman for OpenAI, said in an email interview, adding that the company is continuously trying to improve performance, reduce bias and mitigate harmful outputs. She declined to say how many employees were working on racial bias, or how much money the company had allocated toward the problem.
Black people are accustomed to being unseen, the Senegalese artist Linda Dounia Rebeiz wrote in an introduction to her exhibition In/Visible, for Feral File, an NFT marketplace. When we are seen, we are accustomed to being misrepresented.
To prove her point during an interview with a reporter, Rebeiz, 28, asked OpenAIs image generator, DALL-E 2, to imagine buildings in her hometown, Dakar. The algorithm produced arid desert landscapes and ruined buildings that Rebeiz said were nothing like the coastal homes in the Senegalese capital.
Its demoralizing, Rebeiz said. The algorithm skews toward a cultural image of Africa that the West has created. It defaults to the worst stereotypes that already exist on the internet.
Last year, OpenAI said it was establishing new techniques to diversify the images produced by DALL-E 2, so that the tool generates images of people that more accurately reflect the diversity of the worlds population.
An artist featured in Rebeizs exhibition, Minne Atairu is a doctoral candidate at Columbia Universitys Teachers College who planned to use image generators with young students of color in the South Bronx. But she now worries that might cause students to generate offensive images, Atairu explained.
Included in the Feral File exhibition are images from her Blonde Braids Studies, which explore the limitations of Midjourneys algorithm to produce images of Black women with natural blond hair. When the artist asked for an image of Black identical twins with blond hair, the program instead produced a sibling with lighter skin.
That tells us where the algorithm is pooling images from, Atairu said. Its not necessarily pulling from a corpus of Black people, but one geared toward white people.
She said she worried that young Black children might attempt to generate images of themselves and see children whose skin was lightened. Atairu recalled some of her earlier experiments with Midjourney before recent updates improved its abilities. It would generate images that were like blackface, she said. You would see a nose, but it wasnt a humans nose. It looked like a dogs nose.
In response to a request for comment, David Holz, Midjourneys founder, said in an email, If someone finds an issue with our systems, we ask them to please send us specific examples so we can investigate.
Stability AI, which provides image generator services, said it planned on collaborating with the AI industry to improve bias evaluation techniques with a greater diversity of countries and cultures. Bias, the AI company said, is caused by overrepresentation in its general data sets, though it did not specify if overrepresentation of white people was the issue here.
Earlier this month, Bloomberg analyzed more than 5,000 images generated by Stability AI, and found that its program amplified stereotypes about race and gender, typically depicting people with lighter skin tones as holding high-paying jobs while subjects with darker skin tones were labeled dishwasher and housekeeper.
These problems have not stopped a frenzy of investments in the tech industry. A recent rosy report by the consulting firm McKinsey predicted that generative AI would add $4.4 trillion to the global economy annually. Last year, nearly 3,200 startups received $52.1 billion in funding, according to the GlobalData Deals Database.
Technology companies have struggled against charges of bias in portrayals of dark skin from the early days of color photography in the 1950s, when companies like Kodak used white models in their color development. Eight years ago, Google disabled its AI programs ability to let people search for gorillas and monkeys through its Photos app because the algorithm was incorrectly sorting Black people into those categories. As recently as May of this year, the issue still had not been fixed. Two former employees who worked on the technology told The New York Times that Google had not trained the AI system with enough images of Black people.
Experts who study artificial intelligence said that bias goes deeper than data sets, referring to the early development of this technology in the 1960s.
The issue is more complicated than data bias, said James E. Dobson, a cultural historian at Dartmouth College and the author of a recent book on the birth of computer vision. There was very little discussion about race during the early days of machine learning, according to his research, and most scientists working on the technology were white men.
Its hard to separate todays algorithms from that history, because engineers are building on those prior versions, Dobson said.
To decrease the appearance of racial bias and hateful images, some companies have banned certain words from text prompts that users submit to generators, like slave and fascist.
But Dobson said that companies hoping for a simple solution, like censoring the kind of prompts that users can submit, were avoiding the more fundamental issues of bias in the underlying technology.
Its a worrying time as these algorithms become more complicated. And when you see garbage coming out, you have to wonder what kind of garbage process is still sitting there inside the model, the professor added.
Auriea Harvey, an artist included in the Whitney Museums recent exhibition Refiguring, about digital identities, bumped into these bans for a recent project using Midjourney. I wanted to question the database on what it knew about slave ships, she said. I received a message saying that Midjourney would suspend my account if I continued.
Dinkins ran into similar problems with NFTs that she created and sold showing how okra was brought to North America by enslaved people and settlers. She was censored when she tried to use a generative program, Replicate, to make pictures of slave ships. She eventually learned to outwit the censors by using the term pirate ship. The image she received was an approximation of what she wanted, but it also raised troubling questions for the artist.
What is this technology doing to history? Dinkins asked. You can see that someone is trying to correct for bias, yet at the same time that erases a piece of history. I find those erasures as dangerous as any bias, because we are just going to forget how we got here.
Naomi Beckwith, chief curator at the Guggenheim Museum, credited Dinkins nuanced approach to issues of representation and technology as one reason the artist received the museums first Art & Technology award.
Stephanie has become part of a tradition of artists and cultural workers that poke holes in these overarching and totalizing theories about how things work, Beckwith said. The curator added that her own initial paranoia about AI programs replacing human creativity was greatly reduced when she realized these algorithms knew virtually nothing about Black culture.
But Dinkins is not quite ready to give up on the technology. She continues to employ it for her artistic projects with skepticism. Once the system can generate a really high-fidelity image of a Black woman crying or smiling, can we rest?
This article originally appeared in The New York Times