#Explainability

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

2 posts tagged with explainability. ← All posts

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Pratik Dhanave · ·14 min read

Bias, Fairness, and Explainability

The three trustworthy-AI properties regulators and users press on hardest — where bias enters a system, why the fairness definitions contradict each other so you must choose one deliberately, and why an explanation you can read is not the same as an explanation you can trust.

The trustworthy-AI properties regulators care about: where bias enters, why fairness notions conflict (you must choose one), disaggregated evaluation, mitigation with Fairlearn/AIF360, and explainability (SHAP/LIME) — with the honest caveat that LLM rationales are not faithful explanations.

#AI Governance#Fairness#Explainability#Responsible AI#Governance#LLMs#Evaluation
Pratik Dhanave · ·8 min read

Explaining Credit Decisions: SHAP and Adverse-Action Reason Codes

How lenders turn a model's raw probability into the specific, legally required reasons a declined applicant must receive.

Turning model outputs into legally-required adverse-action reason codes. Global vs local explainability, SHAP values, mapping contributions to reason codes, and fairness checks (disparate impact) that regulators expect.

#Financial AI#Machine Learning#Explainability#Credit#Credit Risk

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.