Eye of the Beholder: Defining AI Bias Depends on Your Perspective

Article by Mike Barlow: “…Today’s conversations about AI bias tend to focus on high-visibility social issues such as racism, sexism, ageism, homophobia, transphobia, xenophobia, and economic inequality. But there are dozens and dozens of known biases (e.g., confirmation bias, hindsight bias, availability bias, anchoring bias, selection bias, loss aversion bias, outlier bias, survivorship bias, omitted variable bias and many, many others). Jeff Desjardins, founder and editor-in-chief at Visual Capitalist, has published a fascinating infographic depicting 188 cognitive biases–and those are just the ones we know about.

Ana Chubinidze, founder of AdalanAI, a Berlin-based AI governance startup, worries that AIs will develop their own invisible biases. Currently, the term “AI bias” refers mostly to human biases that are embedded in historical data. “Things will become more difficult when AIs begin creating their own biases,” she says.

She foresees that AIs will find correlations in data and assume they are causal relationships—even if those relationships don’t exist in reality. Imagine, she says, an edtech system with an AI that poses increasingly difficult questions to students based on their ability to answer previous questions correctly. The AI would quickly develop a bias about which students are “smart” and which aren’t, even though we all know that answering questions correctly can depend on many factors, including hunger, fatigue, distraction, and anxiety. 

Nevertheless, the edtech AI’s “smarter” students would get challenging questions and the rest would get easier questions, resulting in unequal learning outcomes that might not be noticed until the semester is over—or might not be noticed at all. Worse yet, the AI’s bias would likely find its way into the system’s database and follow the students from one class to the next…

As we apply AI more widely and grapple with its implications, it becomes clear that bias itself is a slippery and imprecise term, especially when it is conflated with the idea of unfairness. Just because a solution to a particular problem appears “unbiased” doesn’t mean that it’s fair, and vice versa. 

“There is really no mathematical definition for fairness,” Stoyanovich says. “Things that we talk about in general may or may not apply in practice. Any definitions of bias and fairness should be grounded in a particular domain. You have to ask, ‘Whom does the AI impact? What are the harms and who is harmed? What are the benefits and who benefits?’”…(More)”.