activity
20172026
most citedExpanding Explainability: Towards Social Transparency in AI systems

497 citations · 1.9k across the 38 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.CY2020★ 28 cited

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.LG2020★ 1 cited

Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation

Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1

We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for cho…

cs.HC2020★ 8 cited

Measuring Social Biases of Crowd Workers using Counterfactual Queries

Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1

Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effect…

cs.CY2020★ 24 cited

Business (mis)Use Cases of Generative AI

Stephanie Houde, Vera Liao, Jacquelyn Martino +5

Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs…

cs.HC2020★ 18 cited

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…