activity
20192022
most citedMachine Learning Explainability for External Stakeholders

41 citations · 107 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CY2022

Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA

Rui-Jie Yew, Alice Xiang

Harms resulting from the development and deployment of facial processing technologies (FPT) have been met with increasing controversy. Several states and cities in the U.S. have ba…

stat.AP2021

On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes

Riccardo Fogliato, Alice Xiang, Zachary Lipton +2

The risk of re-offense is considered in decision-making at many stages of the criminal justice system, from pre-trial, to sentencing, to parole. To aid decision makers in their ass…

cs.HC2021

A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms

Ana Lucic, Madhulika Srikumar, Umang Bhatt +4

Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders ha…

cs.CY202016 cited

Affirmative Algorithms: The Legal Grounds for Fairness as Awareness

Daniel E. Ho, Alice Xiang

While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques. We ma…

cs.CY2020

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

Umang Bhatt, Javier Antorán, Yunfeng Zhang +12

Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most…

cs.CY202020 cited

"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness

McKane Andrus, Elena Spitzer, Jeffrey Brown +1

As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often d…