Societal Impact
Bias & Fairness
An AI can copy the unfairness in its data, and call it objective.
1
The mirror problem
An AI trained on the past learns the past, including its prejudices. The system isn't evil; it faithfully copied a biased world and made it look neutral.
2
'The algorithm decided' is never an excuse
Someone chose the data, the goal, and to deploy it. Responsibility never disappears into the code. Asking 'who is helped, who is harmed?' is step one.
3
Fairness is a choice
There are real, conflicting definitions of 'fair.' Engineers and communities must choose openly, which is a human decision, not a math output.
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