Earlier this year, Danielle Cass and I ran a workshop with 23 ethical AI practitioners from 15 organizations and shared out insights of what they are doing that has been successful and the…
Published: July 10, 2019 As a discipline, those of us working on ethical or responsible AI, are learning together how to translate ethical principles into business practices that work for each of our…
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…
In this era of “big data,” nonprofit and higher ed institutions are able to use technology to collect more and more data every day. Whether migrating from legacy on-premise systems with decades of…
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.
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.
