Clinician-facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI
Artificial Intelligence (AI) in medical applications holds great promise. However, the use of
Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely …
Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely …
Expanding explainability: Towards social transparency in ai systems
As AI-powered systems increasingly mediate consequential decision-making, their
explainability is critical for end-users to take informed and accountable actions. Explanations …
explainability is critical for end-users to take informed and accountable actions. Explanations …
Towards a science of human-ai decision making: a survey of empirical studies
As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
Inclusion of clinicians in the development and evaluation of clinical artificial intelligence tools: a systematic literature review
The application of machine learning (ML) and artificial intelligence (AI) in healthcare
domains has received much attention in recent years, yet significant questions remain about …
domains has received much attention in recent years, yet significant questions remain about …
Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …
purposes that include understanding, improving, and contesting predictions. Until now, most …
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities and unfairness
is receiving increasing popular and academic attention. A surge of recent work has focused …
is receiving increasing popular and academic attention. A surge of recent work has focused …
Improving human-AI partnerships in child welfare: understanding worker practices, challenges, and desires for algorithmic decision support
A Kawakami, V Sivaraman, HF Cheng… - Proceedings of the …, 2022 - dl.acm.org
AI-based decision support tools (ADS) are increasingly used to augment human decision-
making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is …
making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is …
How to evaluate trust in AI-assisted decision making? A survey of empirical methodologies
The spread of AI-embedded systems involved in human decision making makes studying
human trust in these systems critical. However, empirically investigating trust is challenging …
human trust in these systems critical. However, empirically investigating trust is challenging …
Human-centered tools for coping with imperfect algorithms during medical decision-making
Machine learning (ML) is increasingly being used in image retrieval systems for medical
decision making. One application of ML is to retrieve visually similar medical images from …
decision making. One application of ML is to retrieve visually similar medical images from …
Re-examining whether, why, and how human-AI interaction is uniquely difficult to design
Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user
experience. Yet many challenges persist in designing and innovating valuable human-AI …
experience. Yet many challenges persist in designing and innovating valuable human-AI …