Eric Ghysels: The situation with ChatGPT is more complex. Our research pertains to biases in binary choice problems, or what machine learning researchers call classification problems.

The challenge of developing a fairness framework lies in problems within the original data used in machine learning technologies. In some instances, the data may lack quality, leading to missing ...

To build on what I am learning from these extremely passionate and knowledgeable undergraduates, I propose to develop a new 200-level course that focuses specifically on ethics and fairness in machine ...

Ensuring transparency means addressing bias and fairness in machine learning systems. Bias in AI can lead to unfair outcomes, like rejecting qualified loan applicants or uneven medical diagnostics.

This focus on bias-aware ML pipelines translates directly into interview readiness. FAANG interviews often include questions on fairness and ML system design, candidates can expect to be grilled on ...

Fujitsu is providing automated machine learning and AI fairness technologies as OSS via the Linux Foundation, enabling developers around the world to access and widely use Fujitsu's technology at ...

Advances in machine learning (ML) provide the opportunity to improve predictions that may expand credit access to more applicants. However, there is concern that gains from advanced models could ...

“This new machine learning study is important because it proposes a new statistical test methodology to evaluate the fairness of AI predictions,” said Chris Roche, Exactech’s Sr. Vice ...

Machine learning models have significant influence over users’ choices and experiences. Addressing fairness and discrimination concerns is vital to ensure equitable treatment for all users.