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We have established a set of five guidelines that build upon our Trusted AI Principles to provide more detailed guidance for the responsible development and implementation of GenAI.

It is not enough to deliver only the technological capabilities of AI – we also have an important responsibility to ensure that AI is safe and inclusive for all. These are our commitments to building AI responsibly.

We summarize insights from our FAccT 2022 CRAFT workshop that focused on Concepts of Fairness & Transparency, Applied RAI Practices, Organizational Approaches to RAI & Cultural Change, and Public Policy & Regulation.

TL;DR:OmniXAI (short for Omni eXplainable AI) is designed to address many of the pain points in explaining decisions made by AI models. This open-source library aims to provide data scientists, machine learning engineers, and researchers with a one-stop Explainable AI (XAI) solution to analyze, debug, and interpret their

Recommendation systems are common in the consumer world. For example, Netflix, YouTube, and other companies use these systems to recommend items you would probably like, based on data about you - such as what items you've consumed (e.g., watched) before. But recommendation systems are not just for

Published: May 30, 2019 With the advent of low-code developer tools, people of varying skill levels and backgrounds — not just those with PhDs — are not only able to leverage the benefits of artificial intelligence (AI), but also build intelligent systems. As access to AI widens and the depth of its

This is my obsessively curated list of research papers and articles on ethics in AI that I have been collecting over the years. Ones in bold are those that I refer back to and found particularly useful. Let me know if I am missing your favorites.

This post is for those responsible for implementing AI systems but do not have a background in data science, statistics, or probability. The intention is to create an approachable introduction to the key concepts to identify potential bias (error) in their training data.

What kind of world do we want for ourselves, those we love, and future society? How do we are organizations, employees, customers, and members of society ensure that technology is in service of society and not the other way around?

In this article, I will focus on mechanisms for removing exclusion from your data and algorithms. Of all the interventions or actions we can take, the advancements here have the highest rate of change.

This is part one of a two-part series about how to build ethics into AI. Part one focuses on cultivating an ethical culture in your company and team, as well as being transparent within your company and externally.