Search Results for author: Jatinder Singh

Found 5 papers, 0 papers with code

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

no code implementations13 May 2022 Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems.

BIG-bench Machine Learning Fairness

Data Justice in Practice: A Guide for Developers

no code implementations12 Apr 2022 David Leslie, Michael Katell, Mhairi Aitken, Jatinder Singh, Morgan Briggs, Rosamund Powell, Cami Rincón, Antonella Perini, Smera Jayadeva, Christopher Burr

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use.

Fairness

Advancing Data Justice Research and Practice: An Integrated Literature Review

no code implementations6 Apr 2022 David Leslie, Michael Katell, Mhairi Aitken, Jatinder Singh, Morgan Briggs, Rosamund Powell, Cami Rincón, Thompson Chengeta, Abeba Birhane, Antonella Perini, Smera Jayadeva, Anjali Mazumder

The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems.

Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems

no code implementations26 Jan 2021 Jennifer Cobbe, Michelle Seng Ah Lee, Jatinder Singh

This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning.

Decision Making

Decision Provenance: Harnessing data flow for accountable systems

no code implementations16 Apr 2018 Jatinder Singh, Jennifer Cobbe, Chris Norval

Specifically, given the concerns regarding ever-increasing levels of automated and algorithmic decision-making, and so-called 'algorithmic systems' in general, we propose decision provenance as a concept showing much promise.

Decision Making

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