Education

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.

Stories of successful immigrants abound in American business lore. It’s a phenomenon firmly rooted in our country’s history. Alexander Graham Bell, the Scotland-born inventor and entrepreneur, founded Bell Telephone, which eventually became AT&T,…

This article was authored by Power of Us HUB community members Pierre Kaluzny, CEO/Founder at Sputnik Moment and Tal Frankfurt, CEO/Founder at Cloud for Good. Why categorize contacts? Most organizations have a need to…

Sometimes in higher ed, we’re our own worst enemy — buying multiple point-solution systems that result in data silos, frustrated users who have multiple login credentials, and no holistic view of the student. It makes…