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Simplicity and probability in causal explanation
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Citations
Cites
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Simplicity: a unifying principle in cognitive science?
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Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
Misinformation and Its Correction: Continued Influence and Successful Debiasing
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2020) · arXiv · doi:10.48550/arxiv.2006.14779 · Cited by 754
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757
“d in the design of many AI explanation algorithms — might be a poor choice for complementary performance. Indeed, while it is known that human explainers and listeners often have different objectives [46], it seems quite problematic to develop AI systems that actively seek to minimize human oversight. A more ideal con…”
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The challenge of crafting intelligible intelligence
Weld, D. S., & Bansal, G. (2019) · Communications of the ACM · doi:10.1145/3282486 · Cited by 253
“According to Lombrozo, humans prefer explanations that are simpler ( i.e. , contain fewer clauses), more general, and coherent ( i.e. , consistent with what the human’s prior beliefs) [26].”
Jacobs, M., Pradier, M. F., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01224-x · Cited by 247
Explanation and abductive inference
Lombrozo, T. (2012) · The Oxford Handbook of Thinking and Reasoning · doi:10.1093/oxfordhb/9780199734689.013.0014 · Cited by 242
Self-citationExplanatory Preferences Shape Learning and Inference
Lombrozo, T. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.08.001 · Cited by 229
Self-citationRepresentation of Principled Connections: A Window Onto the Formal Aspect of Common Sense Conception
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How Cognitive Biases Affect XAI-assisted Decision-making: A Systematic Review
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Conviction Narrative Theory: A theory of choice under radical uncertainty
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An Evaluation of the Human-Interpretability of Explanation
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Self-citationThe role of domain expertise in trusting and following explainable AI decision support systems
Bayer, S., Gimpel, H., & Markgraf, M. (2022) · Journal of Decision Systems · doi:10.1080/12460125.2021.1958505 · Cited by 103
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COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
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Explaining the moral of the story
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“Finally, there are many LLM response features that we did not study or control for (e.g., simplicity of explanations [79], quality of sources [98, 118], and presence of jargon [26]).”
Self-citationArtificial agents’ explainability to support trust: considerations on timing and context
Papagni, G., de Pagter, J., Zafari, S., Filzmoser, M., & Koeszegi, S. T. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01462-7 · Cited by 52
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AI Research Considerations for Human Existential Safety (ARCHES)
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A contrastive account of explanation generation
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“Learning by Thinking” in Science and in Everyday Life
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Self-citationHow can Human Beings Transgress their Biologically Based Views?
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Mohamed, A. S., Fritz, C., Saber, A. M., Ma, Y., Kaur, R., Al-Darwbi, M., Friedrich, D., & Kundur, D. (2026) · arXiv · doi:10.48550/arxiv.2609.00171
“XAI should be designed not merely to explain model behavior in abstract terms, but to directly and precisely address these gaps [58], [183].”
Metadata
- Authors
- LOMBROZO, T.
- Venue
- Cognitive Psychology
- Publisher
- Elsevier BV
- Published
- 2007-11
- Volume
- 55
- Issue
- 3
- Pages
- 232-257
- ISSN
- 0010-0285 (ISSN-L)
- OpenAlex
- W2041029272
- Cited by
- 384
- References
- 2
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Open access
- closed
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