96 citations · 224 across the 6 of their papers we have counts for
7 papers
Fairness under uncertainty in sequential decisions
Michelle Seng Ah Lee, Kirtan Padh, David Watson +2
Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural…
Who Controls the Conversation? User Perspectives On Generative AI (LLM) System Prompts
Anna Neumann, Yulu Pi, Jatinder Singh
System prompts - instructions that shape the behaviour of generative AI systems - strongly influence system outputs and users' experiences. They define the model's guidelines, `per…
AI Safety Evaluations Need To Consider Cascading Effects
Anna Neumann, Jatinder Singh
AI systems comprise a range of interactions across the technical and organisational components of a range of actors. These components work together to provide the systems' function…
Understanding accountability in algorithmic supply chains
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 'ma…
Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions
Alexandra Brintrup, George Baryannis, Ashutosh Tiwari +3
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. Altho…
Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
Kornel Lewicki, Michelle Seng Ah Lee, Jennifer Cobbe +1
"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data,…
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee +4
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. However, there has…