https://inferenceproject.yale.edu/bias-algorithms
Artificial algorithms are increasingly being deployed to inform, endorse, and govern various aspects of today’s society. Their reach includes the domains of hiring, lending, medicine, criminal justice, insurance, allocation of public services, social and business interactions, and the dissemination of information and news. Through a synthesis of computational and statistical models for representing concepts, human-generated datasets that provide examples for training, and powerful optimization algorithms that can efficiently navigate through vast and complex landscapes to infer concepts that explain data, such algorithms have taken big strides towards mimicking various aspects of natural intelligence.
These algorithms have led to tremendous economic and social impact but have also been shown to be biased – they can discriminate, reinforce prejudices, polarize opinions, and influence political processes. How can subjective human or societal biases emerge in the objective world of artificial algorithms? And how can we design algorithms free from these limitations?
The search for answers to these questions also leads us to some understanding of the bias in human decision-making algorithms.
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