Knowledge-Based Causal Attribution: The Abnormal Conditions Focus Model

By Denis J. Hilton, Ben R. Slugoski. 1986

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tim Miller
2. Q. Liao
3. Chris Russell
4. Alon Jacovi
5. Ashraf Abdul
6. Brent Daniel Mittelstadt
7. John McClure
8. Danding Wang

Citations in the corpus

• Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Related to the idea of contrastive explanation, Hilton and Slugoski [77] propose the abnormal conditions model, based on observations from legal theorists Hart and Honoré [64].”
• Explaining Explanations in AI (2018, background): “The perceived abnormality of an event influences requests for contrastive explanations which address why a normal or expected event did not occur (Hilton and Slugoski, 1986; McClure et al., 2003; Samland and Waldmann, 2014).”
• Designing Theory-Driven User-Centric Explainable AI (2019, background): “The needs for explanations are triggered by a deviation from expected behavior [39, 79], such as a curious, inconsistent, discrepant or an anomalous event.”
• Self-Blame Among Sexual Assault Victims Prospectively Predicts Revictimization: A Perceived Sociolegal Context Model of Risk (2007, background): “Because the critical task of attribution is identifying the abnormal condition that brought about an outcome (Hilton & Slugoski, 1986), SA victims may perceive themselves as making that difference.”