Explainability pitfalls: Beyond dark patterns in explainable AI
To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful
effects is important. In this paper, we address an important yet unarticulated type of negative …
effects is important. In this paper, we address an important yet unarticulated type of negative …
Designing creative AI partners with COFI: A framework for modeling interaction in human-AI co-creative systems
Human-AI co-creativity involves both humans and AI collaborating on a shared creative
product as partners. In a creative collaboration, interaction dynamics, such as turn-taking …
product as partners. In a creative collaboration, interaction dynamics, such as turn-taking …
Literature reviews in HCI: A review of reviews
This paper analyses Human-Computer Interaction (HCI) literature reviews to provide a clear
conceptual basis for authors, reviewers, and readers. HCI is multidisciplinary and various …
conceptual basis for authors, reviewers, and readers. HCI is multidisciplinary and various …
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 …
sociotechnical gap-divide between the technical affordances and the social needs …
The who in explainable ai: How ai background shapes perceptions of ai explanations
UPOL EHSAN, Georgia Institute of Technology, USA SAMIR PASSI, Cornell University, USA
Q. VERA LIAO, IBM Research AI, USA LARRY CHAN, Georgia Institute of Technology, USA I …
Q. VERA LIAO, IBM Research AI, USA LARRY CHAN, Georgia Institute of Technology, USA I …
[HTML][HTML] Human-in-the-loop machine learning: Reconceptualizing the role of the user in interactive approaches
The rise of intelligent systems and smart spaces has opened up new opportunities for
human–machine collaborations. Interactive Machine Learning (IML) contribute to fostering …
human–machine collaborations. Interactive Machine Learning (IML) contribute to fostering …
Modeling, replicating, and predicting human behavior: a survey
Given the popular presupposition of human reasoning as the standard for learning and
decision making, there have been significant efforts and a growing trend in research to …
decision making, there have been significant efforts and a growing trend in research to …
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 …
opens the black-box of AI is just as important as opening it. We conduct a mixed-methods …
Ganslider: How users control generative models for images using multiple sliders with and without feedforward information
We investigate how multiple sliders with and without feedforward visualizations influence
users' control of generative models. In an online study (N= 138), we collected a dataset of …
users' control of generative models. In an online study (N= 138), we collected a dataset of …
A literature survey of how to convey transparency in co-located human–robot interaction
In human–robot interaction, transparency is essential to ensure that humans understand and
trust robots. Understanding is vital from an ethical perspective and benefits interaction, eg …
trust robots. Understanding is vital from an ethical perspective and benefits interaction, eg …