Author: Kathleen Walch, Ronald Schmelzer
Publisher: AI & Data Today
Publication Year: N/A
Summary: The following podcast focusses on how building frameworks can help avoid the pitfalls associated with bias and fairness in artificial intelligence (AI). Unchecked, we could use algorithms to reinforce biases in the real world. Inclusivity is key: build your algorithms with the target audience in mind, but mind your edge cases as people are not one-size fits all. Monitoring will help ensure these best practices are continued to be applied.