5 papers
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach
Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney
Today, AI is increasingly being used in many high-stakes decision-making applications in which fairness is an important concern. Already, there are many examples of AI being biased…
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +2
The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to th…
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
Bootstrapping Conversational Agents With Weak Supervision
Neil Mallinar, Abhishek Shah, Rajendra Ugrani +9
Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…