post-hoc explanation
What It Means
Post-hoc explanation is when you take an AI model that works like a black box and add tools afterward to help explain why it made specific decisions. It's like having a translator that can look at what your AI did and then create human-readable explanations for those actions. These explanations can take many forms - text summaries, visual highlights, comparisons to similar cases, or rankings of which factors were most important.
Why Chief AI Officers Care
Post-hoc explanations are essential for regulatory compliance in industries like healthcare, finance, and hiring where you must justify AI decisions to auditors or affected individuals. They also build stakeholder trust by making your AI systems transparent and help your teams debug problems when models make unexpected decisions. Without them, you're essentially asking business users and regulators to trust a system they can't understand.
Real-World Example
A bank uses a complex neural network to approve loans but can't explain why it rejected a specific applicant. They add a post-hoc explanation tool that analyzes the decision and generates a report showing 'Credit score (40% influence), debt-to-income ratio (35% influence), and employment history (25% influence) were the primary factors in this denial.' This explanation helps loan officers communicate with customers and satisfies regulatory requirements for fair lending practices.
Common Confusion
People often think post-hoc explanations reveal exactly how the AI model actually works internally, but they're really educated approximations or simplified summaries. The explanation tool is separate from the original AI model and is making its best guess about what drove the decision.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, post-hoc explanations are critical for clinical decision support systems where physicians need to unde...
Finance: In finance, post-hoc explanations are critical for meeting regulatory requirements like MiFID II's algorithmic transpare...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Post-hoc explainability targets models that are not readily interpretable by design by resorting to diverse means to enhance their interpretability, such as text explanations, visual explanations, local explanations, explanations by example, explanations by simplification and feature relevance explanations techniques. Each of these techniques covers one of the most common ways humans explain systems and processes by themselves."Source: NISTIR_8312_Full
"Post-hoc explainability targets models that are not readily inter- pretable by design by resorting to diverse means to enhance their in- terpretability, such as text explanations, visual explanations, local expla- nations, explanations by example, explanations by simplification and feature relevance explanations techniques. Each of these techniques covers one of the most common ways humans explain systems and processes by themselves."Source: barredo_explainable_2020
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