270 citations · 627 across the 15 of their papers we have counts for
16 papers
Quantitative AI Risk Assessments: Opportunities and Challenges
David Piorkowski, Michael Hind, John Richards
Although AI systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified and have manifest…
Evaluating a Methodology for Increasing AI Transparency: A Case Study
David Piorkowski, John Richards, Michael Hind
In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are crea…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users
Tim Draws, Zoltán Szlávik, Benjamin Timmermans +3
Systems aiming to aid consumers in their decision-making (e.g., by implementing persuasive techniques) are more likely to be effective when consumers trust them. However, recent re…
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…