Dinkins working in her Brooklyn studio in New York. For the past seven years, Dinkins has experimented with AI’s ability to realistically depict Black women smiling and crying. — ©2023 The New York Times Company
ARTIST Stephanie Dinkins has long been a pioneer in combining art and technology in her Brooklyn-based practice. In May she was awarded US$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 intelligence’s ability to realistically depict Black women, smiling and crying, using a variety of word prompts. The first results were lacklustre 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 I’m 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 non-white 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,” 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 OpenAI’s 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.
“It’s demoralising,” 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 world’s population”.
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 analysed 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”.
However, 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 US$4.4 trillion to the global economy annually. Last year, nearly 3,200 startups received US$52.1bil in funding, according to the GlobalData Deals Database.
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 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.
“It’s hard to separate today’s 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”.
Auriea Harvey, an artist included in the Whitney Museum’s 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 museum’s first Art & Technology award.
“Stephanie has become part of a tradition of artists and cultural workers that poke holes in these overarching and totalising theories about how things work,” Beckwith said.
She added that her own initial paranoia about AI programs replacing human creativity was greatly reduced when she realised these algorithms knew virtually nothing about Black culture. — ©2023 The New York Times Company
