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Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems
This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning.
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
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.
Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools.
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