Posts in tag

Fairness


Financial institutions that implement AI early have the most to gain from its use, but also face the largest risks. The often-opaque nature of AI decisions and related concerns of algorithmic bias, fiduciary duty, uncertainty, and more have left implementation of the most cutting-edge AI uses at a standstill. However, a newly released report from …

Microsoft Build 2019 | Designing AI Responsibly Session ID: BRK2003   Designing AI to be trustworthy requires creating solutions that reflect ethical principles that are deeply rooted in important and timeless values: Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency and Accountability. But how can you make sure your AI system reflects those values? …

Our use of Artificial Intelligence is growing along with advancements in the field. It has gone to the point that it is used in riskier areas such as hiring, criminal justice, and healthcare. This is with the hope the AI will provide less biased results compared to humans. In their paper, Jake Silberg and James …

PyCon 2019 | Measuring Model Fairness Speaker: J. Henry Hinnefeld   When machine learning models make decisions that affect people’s lives, how can you be sure those decisions are fair? When you build a machine learning product, how can you be sure your product isn’t biased? What does it even mean for an algorithm to …