Reimagining AI oversight in healthcare: Systemic gaps and structural accountability
Original framing: “Who's really in the loop? Rethinking oversight in AI-assisted health care” — bing news
Historically, medical systems have often excluded marginalized voices and exploited vulnerable populations, as seen in the Tuskegee Syphilis Study and forced sterilization programs. These patterns persist in modern AI systems through biased data and algorithmic discrimination, which disproportionately harm communities of color and low-income populations.
To reimagine AI oversight in healthcare, we must move beyond the narrow focus on human-in-the-loop mechanisms and address the deeper systemic issues of power, bias, and exclusion.
Indigenous and non-Western epistemologies offer alternative models of relational accountability and community-based decision-making that challenge the technocratic assumptions of current AI systems. Historical patterns of medical exploitation and algorithmic discrimination reveal the urgent need for participatory governance and decentralized data control. Scientific evidence underscores the risks of biased AI, while artistic and spiritual traditions remind us of the ethical dimensions of health and well-being. By integrating these dimensions into a holistic framework, we can develop AI systems that are not only technically sound but also ethically grounded, culturally responsive, and socially just.