Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services

The U.S. House has had a few data and machine learning specialists (including DJ Patil) over the past week come and testify. One of these testimony’s is of Rayid Ghani on equitable algorithms. The paper is pretty straight forward, pragmatic, and good ground work. The post is here.

A synopsis (from the paper):

Moving Forward to a More Equitable Society: Our Recommendations

It is critical and urgent for policymakers to act and provide guidelines and/or regulations for both the public and private sector organizations using AI-assisted decision-making processes in order to ensure that these systems are built in a transparent and accountable manner and result in fair and equitable outcomes for society. As initial steps, we recommend:

Expanding the existing regulatory environment to account for AI-assisted decision- making.

Instead of creating a Federal AI regulatory agency across policy areas, we should expand the already existing regulatory frameworks in different policy areas, building on their domain-specific expertise while updating them to account for AI-assisted decision-making.

Creating Trainings, Processes, and Tools to Support Regulatory Agencies in their Expanded Roles

Procuring AI systems should include Key Requirements in the Request for Proposals (RFP) Process

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