When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make society more or less equal: technological forces, supply-side forces, and demand-side forces. The last of these is particularly underemphasized. The use of AI in a product can change how much customers value it — for example, patients who put less stock in an algorithmic diagnosis — which in turn can affect how that product is used and how those working alongside it are compensated.
by the Brookings Institution found that “Black and Hispanic workers … are over-represented in jobs with a high risk of being eliminated or significantly changed by automation.” This isn’t because the algorithms involved are biased, but because some jobs consist of tasks that are easier to automate such that investment in AI is a strategic advantage.
Suppose you discover your doctor uses AI tools for diagnosis or treatment. Would that influence your decision to see them? If so, you are not alone. A found that 60% of U.S. adults would be uncomfortable with their healthcare provider relying on AI to treat and diagnose diseases. In economic terms, they may have lower demand for services that incorporate AI.Our recent research sheds light on why AI-augmentation can lower demand for a variety of goods and services. We found that people often perceive the value and expertise of professionals to be lower when they advertise AI-augmented services.
However, we also found that people are divided in their perceptions of AI-augmented labor. In the survey we conducted, 41% of respondents were what we call “AI Alarmists” — people who expressed reservations and concerns about AI’s role in the workplace. Meanwhile, 31% were “AI Advocates,” who wholeheartedly champion the integration of AI in the labor force. The remaining 28% were “AI Agnostics,” those who sit on the fence, recognizing both potential benefits and pitfalls.
That’s especially true in cases where peoples’ perceived value of AI intersects with bias against marginalized groups. For example, the expertise of professionals from dominant groups is typically assumed, while equally qualified professionals from traditionally marginalized groups often face skepticism about their expertise. In the example above, people are skeptical of doctors’ relying on AI — but that distrust may not play out in the same ways across professionals with varying backgrounds.
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