270 citations · 627 across the 15 of their papers we have counts for
3 papers · 1 filter
Experiences with Improving the Transparency of AI Models and Services
Michael Hind, Stephanie Houde, Jacquelyn Martino +4
AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality…
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…
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy +5
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility f…