no code implementations • 16 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.
no code implementations • 26 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.
no code implementations • 6 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.
no code implementations • 12 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.
no code implementations • 13 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.
no code implementations • 2 Feb 2023 • Kornel Lewicki, Michelle Seng Ah Lee, Jennifer Cobbe, Jatinder Singh
"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools.
no code implementations • 28 Apr 2023 • Jennifer Cobbe, Michael Veale, Jatinder Singh
Academic and policy proposals on algorithmic accountability often seek to understand algorithmic systems in their socio-technical context, recognising that they are produced by 'many hands'.
no code implementations • 19 May 2023 • Alexandra Brintrup, George Baryannis, Ashutosh Tiwari, Svetan Ratchev, Giovanna Martinez-Arellano, Jatinder Singh
While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation.