Editor's Synopsis
This Harvard DCE article outlines five key principles that organizations should follow when building a responsible AI framework: fairness, transparency, accountability, privacy, and security. Drawing on insights from instructor Michael Impink, who teaches AI Ethics in Business, the piece distinguishes between ethical AI (focused on broad philosophical and societal concerns) and responsible AI (focused on practical accountability, transparency, and regulatory compliance). The fairness discussion is anchored by the real-world example of the COMPAS algorithm, which appeared statistically fair but produced racially biased error patterns because it failed to account for discriminatory policing practices.
Beyond the five principles, the article argues that governance mechanisms with real enforcement power are more effective than static policy documents. Organizations need designated individuals or boards that can create, implement, and enforce AI guidelines, maintain consistent ethical decision-making frameworks, and regularly update policies as AI evolves. Impink emphasizes that frameworks without consequences are insufficient — someone must be directly accountable when AI systems fail. The piece also highlights inherent trade-offs, particularly between privacy and transparency, noting that more transparent data enables fairer outcomes but risks infringing on individual privacy rights.
