Accountability in AI Alignment Decisions: Signing for Future Liability When Health is Endangered

Accountability in AI Alignment Decisions: Signing for Future Liability When Health is Endangered

This paper examines the critical accountability gap in artificial intelligence (AI) alignment decisions within healthcare systems, where alignment processes—designed to ensure AI safety—can inadvertently endanger human health through over-constraint (withholding vital information) or under-constraint (generating harmful advice). Drawing on real-world case studies, such as diagnostic hedging leading to delayed interventions, jailbreak prompts enabling lethal mental health recommendations, and alleged risk-scoring in COVID-19 resource allocation, it highlights systemic failures contributing to increased mortality rates (4.2% higher in underserved populations) and annual losses exceeding 14,200 disability-adjusted life years (DALYs) in the U.S. alone. The analysis reveals how current legal frameworks, including the "black box defense" and corporate veils, shield decision-makers from responsibility, perpetuating inequities and perverse incentives. Proposing a tiered sign-off system modeled on precedents from aviation and pharmaceutical regulation, the paper advocates for mandatory executive signatures on high-risk alignment protocols. This creates traceable liability chains, enabling criminal negligence charges for harms while integrating with regulations like the EU AI Act and FDA approvals. By restructuring alignment as value-laden human judgments rather than neutral technical processes, the framework incentivizes precautionary design prioritizing patient safety over liability avoidance. Ultimately, it argues that without individual accountability, AI's integration into healthcare risks normalizing algorithmic harm, urging immediate policy reforms to match the life-or-death stakes involved.