Challenges in addressing bias, fairness, and transparency in AI

Description

Is bias creeping into AI, or is it already present in the data that is fueling algorithms? Fairness in AI poses a difficult and subjective question. Sometimes there can be a trade-off between accuracy and fairness of ML algorithms. AI fairness is tricky because you cannot design a randomized experiment with race or gender. Instead, you need to understand and monitor data throughout the lifecycle of AI from generation, collection, sampling, algorithm engineering, etc.

Join Krishna Gade to learn how to address engineering and organizational challenges and operationalize these concepts in a production AI system—and crucially, create a culture of trust in AI.