Explanation and Abductive Inference

By Tania Lombrozo. 2012

259 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, History and Philosophy of Science, Safety, Robustness & Interpretability

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Explanations relate the event being explained to principles and invoke causal relations and mechanisms (Lombrozo, 2012).”
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences (2017, background): “Most philosophers, psychologists, and cognitive scientists in this ﬁeld assert that all why–questions are contrastive (e.g. see [Hilton, 1990; Lombrozo, 2012; Miller, 2017]), and that when people ask for an explanation “ Why P ?”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Subsequent research confirms this distinction [35, 112, 122, 10, 84, 108].”
Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018, methodology): “Explanations of this form have been inspired by works like (Lombrozo 2006; 2012; Miller 2017) which identify properties of explanations in terms of selectivity , con-trastiveness and mental modeling of the explainee, and our recent work (Chakraborti et al. 2018) also demonstrated the usefulness of…”
Textual Explanations for Self-Driving Vehicles (2018, background): “Visual and Textual Explanations: The importance of explanations for an end-user has been studied from the psychological perspective [17,18], showing that humans use explanations as a guide for learning and understanding by building inferences and seeking propositions or judgments that enrich their p”
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy (2017, background): “Further, authors in [ Lombrozo, 2012 ] also underline that explanations are “typically contrastive... the contrast provides a constraint on what should ﬁgure in a selected explanation...” - this is especially relevant in order for an explanation to be self-contained and unambiguous.”
XOC: Explainable Observer-Classifier for Explainable Binary Decisions (2019, background): “Explanations are useful when they enable solving an independent task (Lombrozo, 2012).”
Grounding Visual Explanations (2018, background): “The importance of explanations for humans has been studied in the field of psychology [14,15], showing that humans use explanations as a guide for learning and understanding by building inferences and seeking propositions or judgments that enrich their prior knowledge.”
Depth and deference: When and why we attribute understanding (2016, background): “A point in favor of Depth Difference, aside from the intuitive pull of cases such as Pritchard’s, is the close conceptual link between understanding and explanation (Keil 2006; Lombrozo 2006, 2012).”
Why Do People Tend to Infer “Ought” From “Is”? The Role of Biases in Explanation (2016, background): “Given that explanations are the primary vehicle through which humans understand the world (e.g., Keil, 2006; Lombrozo, 2012), a bias in these judgments may exert a deep influence on sociomoral reasoning.”
Intuitive Theories (2020, background): “Predictions based on an intuitive theory are intimately linked to explanation (Lombrozo, 2012).”
A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021, background): “For instance, Lombrozo treated counterfactual and contrastive explanations as equivalent assuming hypothesized events non-occurred in reality to be ‘‘counterfactual cases’’ where a subset of these cases forms a contrastive explanation [10].”
Expanding Explainability: Towards Social Transparency in AI systems (2021, background): “Moreover, XAI’s disconnect with the philosophical and psychological grounds of human explanations has been duly noted [71], as best represented by Miller’s call for leveraging insights from the Social Sciences [70].”
Cognitive foundations for science assessment design: Knowing what students know about evolution (2012, methodology): “To illustrate this taxonomy, Lombrozo (2012) considers explanations for why a particular tire is round.”
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering (2018, background): “Thus, the presented augmentations can help the community to gain insight behind the answers, and take a step towards explainable AI (Ribeiro, Singh, and Guestrin 2016; Lombrozo 2012).”
The Nature and Development of Critical-Analytic Thinking (2014, background): “, “this hypothesis fits the evidence best, so it must be true”), and analogical reasoning (e.g., “if we do not attack Russia now because it massed troops at the border of Ukraine, Russia will invade Ukraine in the same way that Iraq invaded Kuwait when Iraq massed its troops at Kuwait’s border and w”
Explaining Explanation, Part 1: Theoretical Foundations (2017, methodology): “Theoretical Foundations We establish the theoretical foundations by referencing three descriptive models: (1) abduction, (2) retrospection and counterfactual reasoning, and (3) prospection, or projection to the future, which involves transfactual reasoning.”