I test AI products for a living, and one thing that never stops surprising me is how often "explainability" is treated as either a checkbox or a vague marketing line. Reviewers will praise a model for being "transparent" or criticize it for being a "black box" without giving readers concrete evidence or a repeatable way to probe that claim. Over the years I've developed a handful of practical templates that I use when evaluating AI-driven products—templates that help me explain, demonstrate, and quantify explainability in ways readers can replicate. Below I share those templates and explain how I apply them in real reviews, with examples you can copy into your own review workflow.
Why explainability matters (and what I mean by it)
When I talk about explainability, I mean the ability for a human—product manager, developer, regulator, or end-user—to understand how an AI system arrives at a decision, probability, or recommendation. Explainability sits on a spectrum from simple transparency (e.g., "this model uses X features") to deep causal narratives (e.g., "these inputs influenced the prediction because..."). Different stakeholders need different levels: customers want understandable reasons for recommendations, engineers need debugging insights, and compliance teams need traceable decisions.
My goal in reviews is not to deliver a theoretical treatise but to give readers the tools to judge whether an AI product's level of explainability is sufficient for their use case. For that I use three repeatable templates: Documentation & Claims, Interactive Probing, and Quantitative Behavioural Tests. Use them together for a full picture.
Template 1 — Documentation & Claims: check what the vendor says
This is where I start: read the docs, the blog posts, the whitepapers, and release notes. The goal is to compare claims against evidence. I make a short table in every review to summarize what the vendor promises versus what they clearly document.
| Claim | Documented Evidence | Notes / Gaps |
|---|---|---|
| Model interpretability | API returns feature attributions (SHAP style) per prediction | Paper mentions method but no parameter details or limits |
| Data provenance | High-level description of training datasets | No dataset size, license, or sample examples provided |
| Human-in-the-loop | Admin UI for feedback and correction | Not clear how feedback updates model; no SLA |
I use this template to flag mismatches. For example, a vendor might claim "real-time explanations" but only provide post-hoc visualizations that take minutes to generate. Calling that real-time is misleading for many product use cases.
Template 2 — Interactive probing: ask the model and inspect answers
Docs are necessary but not sufficient. I actually interact with the product. For a conversational AI or recommendation engine, I craft small, targeted probes that reveal behavior. Here are the patterns I use:
- Control probe: Use a minimally changed input to see sensitivity. Example: swap one word in a prompt and observe attribution changes.
- Edge-case probe: Feed unusual or adversarial inputs to test robustness and whether explanations remain coherent.
- Counterfactual probe: Ask the model "what would have changed if..." to see if it produces plausible, actionable counterfactuals.
- User narrative probe: Present a human-friendly scenario (e.g., a loan denial) and ask for plain-language reasons.
In a recent review of a hires-focused resume screener, I used the control probe pattern. I submitted two resumes that differed only in the candidate's listed university: one from a top-ranked school, one from a local college. The model's score dropped significantly, and the provided feature attribution referenced education as the main factor. That matched expectations, but the attribution did not list which parts of the education entry influenced the score (degree vs. institution). That gap matters for applicants and recruiters.
For each probe I capture: the exact input, the output, the explanation the system provides (if any), and whether the explanation is actionable. This makes reproducibility easy for readers.
Template 3 — Quantitative behavioural tests: measurable explainability
Interactive probes are qualitative. To be rigorous, I run small, reproducible experiments and compute metrics. Here are metrics I use and why:
- Stability under perturbation: Measure how much explanations change when inputs are slightly perturbed. High variance in explanations while predictions stay similar is a red flag.
- Faithfulness: Test whether removing top-attributed features actually changes the prediction. If not, the attribution may be unreliable.
- Completeness: For methods that produce additive attributions, check whether attributions sum to the model output or logit as claimed.
- User-simulated decision impact: Simulate decisions based on explanations and measure downstream accuracy or fairness impacts.
Concretely, for faithfulness I run N examples where I zero out top-k features and observe the change in the model score. I report aggregated numbers (mean change, median, and a simple histogram). That gives readers a numeric sense of whether explanations are actually connected to the model’s internal logic.
How I present explainability findings in a product review
Readers want usable takeaways, so I use a consistent format in my reviews. You can copy this structure:
- Explainability claim: Short quote from vendor materials.
- Evidence: What the docs show and links to the exact sections.
- Interactive probes: 3 representative examples (input + output + explanation).
- Quant metrics: Stability and faithfulness results (with code snippets linked if possible).
- Risk & suitability: Which user groups this level of explainability is adequate for (e.g., consumer recommendation vs. regulated credit decisions).
- How to verify: A short checklist readers can run themselves (API calls, UI steps).
I always include a short "How to verify" because I want readers to be empowered. Example checklist items:
- Request an explanation for 10 diverse inputs and save the outputs.
- Perturb each input slightly and compare top-3 features across runs.
- Run a feature-ablation test on the top attributed features and note prediction changes.
Practical notes and common pitfalls
Based on dozens of reviews, here are practical tips I share with readers:
- Ask for raw attribution values: Visuals are great, but numbers let you run stability and faithfulness tests.
- Beware post-hoc rationalizations: Some tools produce human-friendly explanations that are not causally linked to the model. Test faithfulness.
- Check latency and costs: Explanation generation sometimes multiplies compute costs—important if you need real-time transparency.
- Document the update policy: If explanations change after model updates, you need versioning and archive capability for auditing.
Finally, note that explainability is not a silver bullet for bias or safety. A plausible explanation can still be wrong or misleading. Explainability should be paired with robust testing, monitoring, and human oversight—especially in high-stakes contexts.
If you want, I can turn these templates into a downloadable checklist or a small Jupyter notebook example that runs the faithfulness and stability tests against public models (e.g., Hugging Face hosted models). Tell me the kind of model or product you evaluate most often—chatbots, vision systems, recommendation engines—and I’ll adapt the templates with concrete examples you can run in minutes.
