Human-Centered Artificial Intelligence and Machine Learning

By Mark O. Riedl. Human Behavior and Emerging Technologies, 2019

334 citations recorded by the source corpus.

Humans are increasingly coming into contact with artificial intelligence and machine learning systems. Human-centered artificial intelligence is a perspective on AI and ML that algorithms must be designed with awareness that they are part of a larger system consisting of humans. We lay forth an argument that human-centered artificial intelligence can be broken down into two aspects: (1) AI systems that understand humans from a sociocultural perspective, and (2) AI systems that help humans understand them. We further argue that issues of social responsibility such as fairness, accountability, interpretability, and transparency.

View Paper: https://www.semanticscholar.org/paper/1fccba11583dc9e1030713d61bd65e9e9990e39f

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Citations in the corpus

Attention is not not Explanation (2019, background): “Rudin (2018) defines explainability as simply a plausible (but not necessarily faithful) reconstruction of the decision-making process, and Riedl (2019) classifies explainable rationales as valuable in that they mimic what we as humans do when we rationalize past actions: we invent a story that…”
What is AI Literacy? Competencies and Design Considerations (2020, background): “However, due to the differences between AI and human reasoning, theory of mind is not always a reliable way of making sense of AI [111].”
Artificial Intelligence and Journalism (2019, background): “Scholarship and practice should, therefore, seek to undertake an agenda for studying human-centered AI in journalism (Riedl, 2019)—for instance, by understanding the roles and tasks of jour-nalists in hybrid workflows more thoroughly, studying the ways in which journalists and AI can effectively…”
Attention in Natural Language Processing (2019, result): “Their conclusion is that attention may indeed provide an explanation of a model, if by explanation, we mean a plausible, but not necessarily faithful, reconstruction of the decision-making process, as suggested by Rudin [151] and Riedl [152].”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “…have been developed and deployed to date, human-centered AI (HCAI) seeks to re-position humans at the center of the AI lifecycle (Bond et al., 2019; Riedl, 2019; Xu, 2019) and improve human performance in ways that are reliable, safe, and trustworthy by augmenting rather than replacing human…”
Attention, please! A survey of neural attention models in deep learning (2021, result): “Their conclusion is that changing attention weights does not significantly affect the model’s prediction, contrary to research by Rudin (2018) and Riedl (2019) (Sect.”
Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 (2022, background): “, learners, instructors, and information and resources (Riedl, 2019).”
Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, background): “…n - c o n t r o ll e d A I (Auernhammer, 2020), participatory design (Neuhauser & Kreps, 2011), inclusive design (Spencer et al., 2018), interaction design (Winograd, 2006), human-centered computing (Brezillon, 2003; Ford et al., 2015; Hoffman et al., 2004), and social responsibility (Riedl, 2019).”
Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century (2021, background): “Therefore, it is highlighted that AI “algorithms must be designed with awareness that they are part of a larger system consisting of humans” [53] (p.”