What similar cases influenced this result?
Useful when clients need to explain AI decisions to users who have no statistical background: particularly in healthcare, benefits, and legal contexts where precedent and comparison are natural reasoning modes.
Abstract factor lists and probability scores are cognitively demanding for most users. People understand by comparison: by seeing how their situation relates to others like it. "Your application resembles cases where the primary issue was income instability" is often more meaningful than a bar chart of feature weights.
Example-based explanation draws on how humans naturally reason about unfamiliar decisions: through analogy, precedent, and prototype. In legal reasoning, precedent is the primary basis for judgment. In medicine, differential diagnosis is comparison-driven. In everyday life, people ask "has this happened before?" before trusting a new outcome.
For AI systems, this pattern is particularly valuable when the underlying model logic is complex or opaque: presenting a comparable case can make an otherwise abstract decision feel legible without requiring the model internals to be exposed.
Help users understand an AI decision by showing how it relates to comparable cases: building intuition through analogy rather than abstraction.
Cards showing 2–4 comparable cases with the key attributes that make them similar, the decision outcome, and a brief explanation of relevance. Anonymized where needed.
A signal showing how similar the example case is: "very similar" or "moderately similar", so users can calibrate how much weight to give the comparison.
A two-column layout placing the current case alongside a similar one. Key matching attributes are highlighted; differences are also visible, not hidden.
Instead of individual cases, a summary view: "Of 84 similar applications, 61 were approved, 23 were declined." Gives a population-level sense of how cases like this typically resolve.
A short plain-language description: "Applications with similar income levels and employment gaps are typically referred for manual review." Translates the comparison into a readable statement rather than a visual.
A specific example showing a case that is similar except for one key factor, and had a different outcome. This is a bridge between Example-Based Explanation and Counterfactual Explanation: "Here's a case nearly identical to yours that was approved: the main difference was verified income documentation."
A two-example format drawn from interpretable machine learning: a prototype is a representative case that shows what a typical outcome of this type looks like; a criticism is a case that is unusual or hard to classify: one that the prototype does not fully explain. Showing both together gives users a richer sense of the decision space than any single comparison. Useful for professional audiences (clinicians, analysts) who reason by analogy and benefit from understanding both the rule and its exceptions.
Case-based reasoning research shows that clinicians, lawyers, and analysts routinely solve new problems by retrieving and adapting solutions from memory. Example-based explanation meets these users where they already are: it doesn't ask them to adopt a new cognitive model. For lay users, examples still outperform abstract feature lists because they anchor the explanation in something concrete and comparable.
Real cases carry more credibility. Where privacy prevents disclosure, anonymize by removing or generalizing identifying attributes, but be explicit that the examples have been anonymized.
"Similar case" means nothing without knowing in what way it's similar. Always surface the attributes driving the comparison.
Showing only the single most similar case can create a false sense of precision. Showing 2–3 cases with different outcomes gives a more honest picture of the decision space.
Comparison is not causation. A user seeing three similar declined cases might conclude "cases like mine always fail": even if their specific situation is addressable. Pair examples with Actionable Recourse to prevent fatalism.
Users should understand whether similarity is being calculated on all attributes, a subset, or a specific focus area.
Explanation vs. privacy
Real cases are more credible than synthetic ones but raise data protection concerns. Establish a clear policy: anonymize, aggregate, or generate synthetic cases. Be transparent with users about which approach is used.
Similarity vs. misleading analogy
Two cases can be superficially similar but differ in ways that matter greatly. A comparison that looks reassuring may actually be inapplicable. Use similarity scores and show differences, not just matches.
Intuition vs. false precedent
Example-based explanation can create an impression of legal or policy precedent where none exists. Be clear that similar past outcomes do not guarantee or constrain future decisions.
Attribution explains factors within a case. Example-based explanation explains the decision by reference to other cases. They answer the same underlying question through different cognitive approaches.
The "near-miss" variant of example-based explanation is functionally a counterfactual. The two patterns overlap and can be combined.
Showing a case that was approved due to one different factor creates a natural bridge to recourse: "here's what that person had that you could also provide."
Expert users (clinicians, analysts) benefit from richer case detail; lay users benefit from summary comparisons. The same underlying case data can be presented at different depths by role.
Lipton (2018) — The Mythos of Model Interpretability
argues that interpretability is not a single property and that different users need different forms of explanation. Example-based approaches are identified as naturally aligned with how domain experts (clinicians, lawyers) already reason
Aamodt & Plaza (1994) — Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
foundational paper establishing case-based reasoning as a cognitive model and AI methodology. The pattern draws on this tradition: humans solve new problems by retrieving and adapting solutions from similar past cases, a process CBR formalizes. Published in AI Communications, 7(1), pp. 39–59
Molnar (2022) — Interpretable Machine Learning
comprehensive reference covering example-based methods including influential instances, prototypes, and counterfactuals. Provides the technical context for the UI decisions in this pattern