Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI

When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make society more or less equal: technological forces, supply-side forces, and demand-side forces. The last of these is particularly underemphasized. The use of AI in a product can change how much customers value it — for example, patients who put less stock in an algorithmic diagnosis — which in turn can affect how that product is used and how those working alongside it are compensated.

Social Enterprise Startup Focuses on Women's Health - News

MKAI Inclusive AI Forum August 2021: What will it take to solve the

Biased Algorithms Exacerbate Racial Inequality in Health Care

Algorithmic bias in machine learning-based marketing models

AI Bias: Definition, Occurrence, Types, Causes, and Prevention

Generative AI in VC Investment: Expert Q&A

How AI developers can assure algorithmic fairness

Podcast Episode: Algorithms for a Just Future

New Emory institute harnesses the power of AI to improve health

Addressing bias in health care

Navigating Bias and Fairness Challenges in AI/ML Development