Talking Back -- human input and explanations to interactive AI systems
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Abstract (arXiv)
While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely with human concepts.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 6 Mar 2025.
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4 of 28 references.
References · 4
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
- 2024Pix2Code: Learning to Compose Neural Visual Concepts as Programscited by 18
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