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docs(research): introduce research & validation layer for TrustLens - #113
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📝 WalkthroughWalkthroughThis PR adds a comprehensive Research & Validation documentation layer to TrustLens. New research pages document the methodology, benchmark results, failure modes, and metric limitations. The documentation section is integrated into the project navigation and changelog. ChangesResearch & Validation Documentation
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Actionable comments posted: 1
🧹 Nitpick comments (3)
docs/research/why_trustlens.md (2)
8-8: ⚡ Quick winStandardize benchmark artifact path for traceability consistency.
This page references
trustlens_model_zoo_benchmark.ipynb, while other research pages useexamples/trustlens_model_zoo_benchmark.ipynb. Use one canonical path everywhere to avoid ambiguity during evidence audits.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@docs/research/why_trustlens.md` at line 8, The document references the benchmark notebook using a non-canonical path; update the reference in why_trustlens.md to use the canonical path "examples/trustlens_model_zoo_benchmark.ipynb" so it matches other research pages, and scan for other occurrences of "trustlens_model_zoo_benchmark.ipynb" to replace any stray non-canonical paths; specifically edit the line containing the phrase "trustlens_model_zoo_benchmark.ipynb" in why_trustlens.md (and any other docs that differ) to the unified "examples/trustlens_model_zoo_benchmark.ipynb".
46-48: ⚡ Quick winSoften deterministic deployment language to stay evidence-aligned.
Phrases like “you will deploy” and “resulting in” read as absolute outcomes. Recommend probabilistic wording (“may”, “can”, “is likely to”) to keep the claims scientifically scoped.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@docs/research/why_trustlens.md` around lines 46 - 48, Replace deterministic phrasing in the two sentences to probabilistic language: change "you will deploy Model A, resulting in silent failures and demographic bias in production." to something like "you may deploy Model A, which can contribute to silent failures and demographic bias in production." and change "Model A receives a failing verdict, providing the engineering team with diagnostic signals to address the underlying data imbalance before release." to "Model A may receive a failing verdict, providing the engineering team with diagnostic signals to address the underlying data imbalance before release." Use modal verbs ("may", "can", "is likely to") and phrasing ("which can contribute to", "may receive") to soften certainty while preserving the original contrast between Accuracy/ROC-AUC and TrustLens.docs/research/trust_score_validation.md (1)
6-6: ⚡ Quick winUse the same benchmark path format as the rest of research docs.
For consistency and auditability, align this reference with the canonical artifact path used elsewhere (e.g.,
examples/trustlens_model_zoo_benchmark.ipynb).🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@docs/research/trust_score_validation.md` at line 6, Update the artifact reference in docs/research/trust_score_validation.md so the inline notebook path uses the canonical examples path: replace the mention of "trustlens_model_zoo_benchmark.ipynb" with "examples/trustlens_model_zoo_benchmark.ipynb" (keep surrounding sentence text unchanged) to match other research docs and ensure consistent auditability.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@README.md`:
- Line 273: The contributor avatar <img> tag is missing an alt attribute which
triggers MD045; update the <img> element shown (<img
src="https://github.com/q404365631.png" width="36" style="border-radius:50%" />)
to include a meaningful alt value (e.g. alt="Contributor q404365631 avatar" or
similar descriptive text) so the image is accessible to screen readers.
---
Nitpick comments:
In `@docs/research/trust_score_validation.md`:
- Line 6: Update the artifact reference in
docs/research/trust_score_validation.md so the inline notebook path uses the
canonical examples path: replace the mention of
"trustlens_model_zoo_benchmark.ipynb" with
"examples/trustlens_model_zoo_benchmark.ipynb" (keep surrounding sentence text
unchanged) to match other research docs and ensure consistent auditability.
In `@docs/research/why_trustlens.md`:
- Line 8: The document references the benchmark notebook using a non-canonical
path; update the reference in why_trustlens.md to use the canonical path
"examples/trustlens_model_zoo_benchmark.ipynb" so it matches other research
pages, and scan for other occurrences of "trustlens_model_zoo_benchmark.ipynb"
to replace any stray non-canonical paths; specifically edit the line containing
the phrase "trustlens_model_zoo_benchmark.ipynb" in why_trustlens.md (and any
other docs that differ) to the unified
"examples/trustlens_model_zoo_benchmark.ipynb".
- Around line 46-48: Replace deterministic phrasing in the two sentences to
probabilistic language: change "you will deploy Model A, resulting in silent
failures and demographic bias in production." to something like "you may deploy
Model A, which can contribute to silent failures and demographic bias in
production." and change "Model A receives a failing verdict, providing the
engineering team with diagnostic signals to address the underlying data
imbalance before release." to "Model A may receive a failing verdict, providing
the engineering team with diagnostic signals to address the underlying data
imbalance before release." Use modal verbs ("may", "can", "is likely to") and
phrasing ("which can contribute to", "may receive") to soften certainty while
preserving the original contrast between Accuracy/ROC-AUC and TrustLens.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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📒 Files selected for processing (11)
.gitignoreCHANGELOG.mdREADME.mddocs/index.mddocs/research/benchmark_results.mddocs/research/distribution_shift.mddocs/research/failure_modes.mddocs/research/methodology.mddocs/research/metric_limitations.mddocs/research/trust_score_validation.mddocs/research/why_trustlens.md
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AI Code Review by LlamaPReview
🎯 TL;DR & Recommendation
Recommendation: Approve with suggestions.
This PR adds a comprehensive research documentation layer for TrustLens, enhancing scientific rigor with empirical evidence and transparent limitations. However, two minor issues should be addressed: a missing navigation update in mkdocs.yml and a lack of automated validation linking documentation claims to the benchmark notebook.
📄 Documentation Diagram
This diagram illustrates how the benchmark notebook feeds into the new research documentation pages and how users access them.
sequenceDiagram
participant B as Benchmark Notebook
participant R as Research Docs
participant U as User
B->>R: Generate empirical evidence (figures, claims)
note over R: PR #35;113: 7 new research pages
U->>R: Read docs via index.md or sidebar (if mkdocs.yml updated)
note over U: User gains scientific context and limitations
🌟 Strengths
- Thorough scientific methodology and transparent disclosure of limitations.
- High-quality publication-ready visuals with consistent styling.
💡 Suggestions
- docs/index.md: The new research section is missing from
mkdocs.ymlnavigation, which will prevent the pages from appearing in the sidebar. Add the corresponding nav entry. - docs/research/benchmark_results.md: Claims of evidence traceability rely on an external notebook not validated by CI; consider adding a version pin or a notebook execution check to prevent drift.
💡 Have feedback? We'd love to hear it in our GitHub Discussions.
✨ This review was generated by LlamaPReview Advanced, which is free for all open-source projects. Learn more.
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Summary
This PR introduces a comprehensive Research & Validation documentation layer for TrustLens, transforming the benchmark and demo assets into a scientifically grounded, research-grade documentation experience.
The goal is to make TrustLens more transparent, reproducible, and evidence-driven by documenting:
This work treats the benchmark notebook as the scientific source of truth, with all claims grounded in reproducible benchmark evidence.
What’s Included
Research Documentation
Added a new
docs/research/section with seven research-focused pages:benchmark_results.md— structured analysis of benchmark findingsmethodology.md— experimental setup, reproducibility, and threats to validitytrust_score_validation.md— scientific interpretation of the Trust Scoredistribution_shift.md— degradation behavior under corruption and driftmetric_limitations.md— transparent disclosure of assumptions and weaknessesfailure_modes.md— scenarios where TrustLens may be misledwhy_trustlens.md— explains why trust assessment requires more than accuracyPublication-Quality Visuals
Generated high-resolution (300 DPI) research figures in:
Visuals were generated using the centralized TrustLens styling system (
trustlens.visualization.style) to maintain consistency with the rest of the framework.Each visualization follows a scientific interpretation structure:
to improve rigor and readability.
Scientific Rigor Improvements
This PR intentionally prioritizes academic precision and transparency:
Removed overclaiming language ("ensures", "guarantees", "prevents")
Replaced with evidence-based scientific phrasing
Added a brutally honest Threats to Validity section covering:
Documentation Navigation
Updated
docs/index.mdand the documentation navigation to surface the new:section in the docs site.
Source of Truth
Evidence priority for scientific claims:
trustlens_model_zoo_benchmark.ipynb(primary source)trustlens_demo.ipynb(supplementary examples)demo.py(illustrative visual examples only)Why This Matters
TrustLens now moves beyond simply exposing trustworthiness metrics and begins to scientifically justify and contextualize them.
Instead of asking users to trust the framework, this documentation is designed to help users evaluate:
This strengthens:
Verification
Summary by CodeRabbit
Release Notes
Documentation
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