Mark O Riedl

schoolGeorgia Institute of Technologybar_chart#1492 Global Rank

Total Papers

HCI Rank

#1492

Research Areas

Years Active

Recent Publications

From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms to Foster Dignified Human-AI Interaction

In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI's dual role as enhancer and e…

CSCW '244th AuthorSession 3e: Trust and Understanding in Explainable AI

Seamful Explainable AI (XAI): Operationalizing Seamful Design in XAI

Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. While black-boxing AI systems can make the user experience seamless, hiding the seams risks disempowering users to mitigate fallouts from AI mistakes. While Explainable AI (XAI) has predominantly tackled algorithmic…

CHI '24CorrespondingExplainable AI (XAI) +1

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

Explainability of AI systems is critical for users to take informed actions. Understanding who opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups—people with and without AI background—perceive different types of AI explanations. Quantitatively, we…

CSCW '23CorrespondingXAI 1

Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI

Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap—divide between the technical affordances and the social needs. However, charting this gap is challenging. In the context of XAI, we argue that charting the gap improves our problem understanding, which can refle…

CHI '214th AuthorHonorable MentionExplainable AI (XAI) +2

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 in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approach…

IUI '19CorrespondingExplainable AI (XAI) +1

Automated Rationale Generation: a Technique for Explainable AI and its Effects on Human Perceptions

{\em Automated rationale generation} is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like e…

CHI '19CorrespondingGame UX & Player Behavior +2

Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators

Machine learning advances have afforded an increase in algorithms capable of creating art, music, stories, games, and more. However, it is not yet well-understood how machine learning algorithms might best collaborate with people to support creative expression. To investigate how practicing designers perceive the role…

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