#Recourse

Articles about Recourse — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with recourse. ← All posts

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Pratik Dhanave · ·6 min read

Counterfactual and Example-Based Explanations

Feature attributions tell you which inputs mattered, but humans often understand decisions better through a different lens: "what would have to change for a different outcome?" and "what similar cases did the model see?" Counterfactual and example-based explanations answer exactly those questions — and they're often more actionable and more intuitive than attribution scores. This post covers the explanation styles that speak the way people actually reason.

Humans understand decisions by contrast ('what would change the outcome?') and example ('what similar cases?'). Counterfactual explanations give actionable recourse ('approved if income were $5k higher'); example-based explanations build trust by analogy. No single explanation is right — match the style to the question and audience.

#Explainable AI#Counterfactuals#Interpretability#Recourse#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.