265 citations · 566 across the 18 of their papers we have counts for
9 papers · 1 filter
What is my data worth? From data properties to data value
Kalapriya Kannan, Rema Ananthanarayanan, Sameep Mehta
Data today fuels both the economy and advances in machine learning and AI. All aspects of decision making, at the personal and enterprise level and in governments are increasingly…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…
Extracting Fairness Policies from Legal Documents
Rashmi Nagpal, Chetna Wadhwa, Mallika Gupta +3
Machine Learning community is recently exploring the implications of bias and fairness with respect to the AI applications. The definition of fairness for such applications varies…
Efficiently Processing Workflow Provenance Queries on SPARK
Rajmohan C, Pranay Lohia, Himanshu Gupta +3
In this paper, we investigate how we can leverage Spark platform for efficiently processing provenance queries on large volumes of workflow provenance data. We focus on processing…
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
Matthew Arnold, Rachel K. E. Bellamy, Michael Hind +10
Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainabil…
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
Nishtha Madaan, Sameep Mehta, Shravika Mittal +1
The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man B…