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Found 13 papers, 1 papers with code
Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems
We find out that MI-CC systems with more extensive coverage of the design space are rated higher or on par on a variety of creative and goal-completion metrics, demonstrating that wider coverage of the design space can improve user experience and achievement when using the system; Preference varies greatly between expertise groups, suggesting the development of adaptive, personalized MI-CC systems; Participants identified new design space dimensions including scrutability -- the ability to poke and prod at models -- and explainability.
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap.
Seamful XAI: Operationalizing Seamful Design in Explainable AI
We found that the Seamful XAI design process helped users foresee AI harms, identify underlying reasons (seams), locate them in the AI's lifecycle, learn how to leverage seamful information to improve XAI and user agency.
Social Construction of XAI: Do We Need One Definition to Rule Them All?
There is a growing frustration amongst researchers and developers in Explainable AI (XAI) around the lack of consensus around what is meant by 'explainability'.
The Algorithmic Imprint
When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE).
Explainability Pitfalls: Beyond Dark Patterns in Explainable AI
To make Explainable AI (XAI) systems trustworthy, understanding harmful effects is just as important as producing well-designed explanations.
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Explainability of AI systems is critical for users to take informed actions.
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally.
Expanding Explainability: Towards Social Transparency in AI systems
We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level.
Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach
In this paper, we introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design.
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior.
Guiding Reinforcement Learning Exploration Using Natural Language
We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments.
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Results of these evaluations show that neural machine translation is able to accurately generate rationalizations that describe agent behavior, and that rationalizations are more satisfying to humans than other alternative methods of explanation.
