Understanding Human Impact: Social and Equity Assessments for AI Technologies
summary
Describes a concrete framework that adapts environmental impact assessments into social and equity impact assessments for ML products, backed by hiring social scientists and a governance layer tied to Google’s AI principles. Methods include adversarial testing (journalistic break-the-product exercises), focus groups, surveys, expert interviews, and data disaggregation down to census-tract level. Uses a USDA-based food-access example to show how inputs, logic models and mitigations map to specific risks and tradeoffs, and where no mitigation yet exists.
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