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Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice
Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems.
Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust
Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals.
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.
Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation
In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation.
