8 citations · 16 across the 4 of their papers we have counts for
6 papers
A Methodology for Creating AI FactSheets
John Richards, David Piorkowski, Michael Hind +2
As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are develop…
Trust and Transparency in Contact Tracing Applications
Stacy Hobson, Michael Hind, Aleksandra Mojsilovic +1
The global outbreak of COVID-19 has led to focus on efforts to manage and mitigate the continued spread of the disease. One of these efforts include the use of contact tracing to i…
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
Michael Hind, Dennis Wei, Yunfeng Zhang
Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-d…
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…