A review that argues ethical principles for household agentic AI must be converted into concrete design patterns for tailored explainability, granular consent, and user override, especially for vulnerable groups.
Fairlearn: Assessing and Improving Fairness of AI Systems
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abstract
Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems. The associated Python library, also named fairlearn, supports evaluation of a model's output across affected populations and includes several algorithms for mitigating fairness issues. Grounded in the understanding that fairness is a sociotechnical challenge, the project integrates learning resources that aid practitioners in considering a system's broader societal context.
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Advancing Responsible Innovation in Agentic AI: A study of Ethical Frameworks for Household Automation
A review that argues ethical principles for household agentic AI must be converted into concrete design patterns for tailored explainability, granular consent, and user override, especially for vulnerable groups.