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Join and engage in a lively discussion, please bring your professional experience! (In person and Virtual)

Toward Algorithmic Justice in Precision Medicine, a workshop, will convene community members along with health systems professionals, biomedical investigators, and artificial intelligence (AI) tool developers to consider systematic flaws in data collection, datasets, and AI tools that perpetuate or exacerbate structuralized inequities, and how resultant algorithmic injustices can be uncovered and addressed, enabling the equitable implementation of precision medicine.

Introductory Speakers
-Welcoming remarks - Chancellor Sam Hawgood
-Workshop goals and framing - Keith Yamamoto, Vice Chancellor of Science Policy and Strategy, Director of Precision Medicine
-Keynote - Alondra Nelson, Harold F. Linder Professor at the Institute for Advanced Study, former acting director of the White House Office of Science and Technology Policy
-Introduction to workshop topics - Ida Sim, UCSF Chief Research Informatics Officer

Three Focus Areas
1) Advance toward transparency and explainability in healthcare algorithms and their use
2) Engage patients and communities in all phases of healthcare algorithm lifecycle and earn trustworthiness
3) Ensure accountability, equity, and justice in outcomes from healthcare algorithms

Sponsoring Organizations
• UCSF Precision Medicine (PM)
• UCSF Research Development Office (RDO)
• UCSF Office of the Chief Informatics Officer (CRIO)
• UCSF Bakar Computational Health Sciences Institute (BCHSI)
• UCSF UC Berkeley Joint Program in Computational Precision Health (CPH)
• UCSF Clinical and Translational Science Institute (CTSI) Community Engagement Program (CE)
• UCSF Clinical and Translational Science Institute (CTSI) Research Action Group for Equity (RAGE)
• UCSF Office of the Associate Vice Chancellor for Research – Inclusion, Diversity, Equity, and Anti-Racism (VC-IDEA)

Detailed description
Precision medicine seeks to fundamentally alter current policy and practice of biomedical research, public health, and healthcare. It aims to leverage algorithms, including artificial intelligence (AI) tools, to aggregate, integrate, and analyze vast amounts of data from basic science, clinical, personal, environmental, social, and population health settings. It would define biological processes and determine disease mechanisms; develop and deliver precise diagnostics, therapeutics and prevention measures in a manner that advances equity; and advise and treat all people based on their individual conditions, needs, and values. 

However, public health and social science research have shown that certain data collection methods and analyses, constructed datasets, and analytical algorithms carry biases that perpetuate or exacerbate structuralized racism, gender inequities, inaccessibility, and other harms. Therefore, altering current practices and advancing toward precision medicine will require acknowledging and addressing these harms. As part of this workshop, we will consider systemic flaws in data collection efforts, datasets, and AI tools, and how resultant algorithmic injustices can be uncovered and addressed. 

This gathering will convene community members along with health system professionals, biomedical investigators, and AI tool developers to discuss key issues, identify and prioritize specific challenges, and propose actionable recommendations.

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