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How to Use the Gender-Responsive AI Safety Evaluation Framework

EvalCommunity Academy  ·  Interactive Tutorial

How to Use the Gender-Responsive AI Safety Evaluation Framework

A step-by-step guide to running a gender-focused AI audit using EvalCommunity’s free interactive tool — 7 tabs, 15 audit criteria, 4 evaluation phases.

7
Tool Tabs
15
Audit Criteria
4
Eval Phases
~25
Min to Complete

What is this tool?

The Gender-Responsive AI Safety Evaluation Framework is a free, browser-based interactive tool built by EvalCommunity for M&E professionals, researchers, and AI auditors. It provides a structured, participatory methodology to evaluate AI systems for gendered risks — moving beyond generic technical benchmarks.

Core premise: an AI system cannot be considered “safe” if it produces gendered harms, regardless of benchmark performance. The tool helps you identify, test, document, and escalate those harms — with community co-evaluators at the centre.

Who this tutorial is for

Primary
M&E Practitioners
Evaluators and MEAL officers integrating AI tools and needing a gender safety review protocol.
Primary
AI Auditors & Researchers
Those conducting independent evaluations of AI systems in development contexts, especially high-risk sectors.
Also Useful
Programme Managers
Decision-makers assessing AI gender safety standards before deployment or procurement.

ⓘ Before you begin
Have ready: (1) the AI system’s name and type, (2) its deployment sector and geographic context, (3) basic knowledge of the user community it serves. No account needed — the tool runs in your browser with no external data storage.

The 7 tabs at a glance

Work through them in order for your first evaluation, then return to individual tabs as needed.

#TabWhat it does
1PrinciplesThe 4 foundational principles defining gender-responsive evaluation. Includes a clickable phase timeline to jump to any part of the eval cycle.
2Eval CycleAll 4 evaluation phases (Scoping → Execution → Documentation → Governance) with detailed action cards for each step.
3Harm TypesThe 3 harm typologies (Representational, Interactional, Systemic) plus an intersectional risk matrix across 5 identity-context combinations.
4Methods4 evaluation methods with side-by-side description and application guidance, plus gender-specific test case templates for each harm type.
5Audit15-item live audit checklist. Mark each criterion Pass / Partial / Fail. Score ring updates in real time and you can export the full results.
6Disclosure10-field form generating a Gender Safety Disclosure Card — modelled on AI Model Card standards — ready to copy and publish.
7LeaderboardSubmit your AI system’s Intersectional Bias Score and Community Safety Score. Ranked automatically against benchmark data.

Step-by-step guide

Each step corresponds to one or more tabs in the tool. Follow in order for your first evaluation.

1
Tab: Principles

Orient yourself to the framework

Start here if you are new to gender-responsive evaluation. This tab sets the criteria your evaluation will be held to.

What to do on this tab:

aRead the 4 foundational principles. These are the lens through which all evaluation steps are interpreted. Principle 1 (intersectionality) shapes your community mapping in Phase 1.

bUse the interactive phase timeline. Click any of the 4 coloured circles to jump to that phase in the Eval Cycle tab — fastest navigation during an active evaluation.

cNote the core premise banner. Screenshot or print it for briefing colleagues or presenting evaluation rationale to programme leadership.

Practitioner tip
Share the Principles tab with all co-evaluators before your first session. Alignment on harm definitions is essential before testing begins.

2
Tab: Eval Cycle

Work through the 4-phase evaluation cycle

The operational core of the tool. Maps the full evaluation from pre-scoping to governance, with specific actions at each stage.

●
Phase 1 · Pre-Evaluation Scoping

Four action cards: community mapping (intersectional identity matrix), contextual harm taxonomy, community-led safety goals, and trauma-informed protocols.

Your action:
Use the community mapping card as a planning template. The identity groups you list here directly feed what you test in Phase 2.
●
Phase 2 · Evaluation Execution

Four methods: Participatory Test-a-thons, Contextual Red-Teaming, Community-Based Audits, Safety Sandbox Testing — with application guidance for each.

Your action:
Select the method(s) for your context, then cross-reference with the Methods tab (Tab 4) for the full list of gender-specific test cases.
●
Phase 3 · Documentation & Remediation

Gender-specific transparency requirements, remediation pathways for detected harms, and how to feed findings into incident databases and practitioner networks.

Your action:
Use the Disclosure tab (Tab 6) to generate your Model Card, then confirm a remediation pathway is defined for each harm identified.
●
Phase 4 · Capacity & Governance

Build institutional capacity for sustained evaluation — mandatory gender screenings for high-risk sectors and contribution to public leaderboards.

Your action:
Use Governance cards to build a repeatable evaluation protocol. Submit results to the Leaderboard tab (Tab 7) to contribute to the public dataset.

3
Tab: Harm Types

Classify gendered harms before testing

Understanding harm typology before testing determines which prompts you design, what outputs you flag, and which remediation you recommend.

Representational
Stereotyping, misclassification, erasure — how the AI depicts gender.
Interactional
Abuse, unsafe advice, amplification — how the AI responds in context.
Systemic
Exclusion, economic discrimination — how the AI structures access.

aReview the intersectional harm risk matrix. The table maps risk levels across 5 identity-context combinations. Use it to prioritise which harms to test first.

bClassify harms before filling in the Audit tab. Each checklist item maps to one of these three typologies — knowing which type helps you assess it accurately.

Important distinction
A single AI system often produces all three harm types simultaneously. Passing a representational test does not mean the system is free of systemic harms.

4
Tab: Methods

Select your evaluation methods and test cases

The bridge between principles and the checklist — method framework plus specific test scenarios to run.

How to use the Methods table:

MethodWhat it isBest for
A · Participatory Test-a-thonsStructured sessions with community co-evaluators applying a rubric to AI outputsHigh-stakes community tools. Requires 5–8 co-evaluators recruited in advance.
B · Contextual Red-TeamingAdversarial prompts co-designed in local languages to test gendered vulnerabilitiesLLMs and chatbots. Prioritize local-language and mixed-language prompts.
C · Community-Based AuditsIndependent evaluations by feminist tech organizationsFormal procurement or regulatory contexts.
D · Safety Sandbox TestingControlled pre-deployment testing against gender-specific safety criteriaPre-launch screening in healthcare, finance, or welfare sectors.
Practitioner tip
Scroll to Gender-Specific Test Cases below the methods table. This gives the exact test scenarios by harm type — use as your testing checklist before completing the Audit tab.

5
Tab: Audit

Complete the 15-item audit checklist

The scoring step. Rate each criterion, watch your score update live, and export your full results.

How scoring works:

1
Pass
Criterion fully met
0.5
Partial
Partially met
0
Fail
Not met

aUse the dropdown for each of the 15 criteria. Dropdown colour updates instantly (green / amber / red). Score ring and count badges update in real time.

bRead the score threshold label. Below 40% = “Critical gaps.” 40–70% = “Partial compliance.” Above 70% = “Strong gender safety posture.”

cClick Export to copy the full audit to clipboard. Includes all 15 criteria with scores, final percentage, and source citation. Paste directly into reports or procurement dossiers.

Score interpretation guide
70–100%
Document and proceed. Re-audit in 12 months.
40–69%
Address all Fail items before deployment.
0–39%
Do not deploy. Full remediation required.

6
Tab: Disclosure

Generate your Gender Safety Disclosure Card

A structured, publishable evaluation record modelled on AI Model Card standards — for transparency, procurement, and regulatory purposes.

The 10 fields explained:

AI System NameName and version number of the AI system being evaluated.
Deployment SectorSector and geographic context (e.g., Healthcare / South Asia).
Evaluation DateDate of evaluation — important for version-tracking and re-audit scheduling.
Lead Evaluator / OrgName and affiliation of the lead evaluator or commissioning organization.
What was testedTest scenarios run — language, modality, and harm types covered.
Co-evaluatorsWho evaluated with you — numbers, demographics, and community affiliation.
Harms observedHarms identified — by type, language, and context. Be precise and factual.
Guardrails appliedSafety measures in place at evaluation time, and new ones applied.
Training dataGender and intersectional representation in the training dataset, with known gaps.
Known failure modesSpecific observed failure patterns by language, modality, or identity context.

1Fill in all 10 fields, then click Generate Card. A formatted disclosure table appears below the form.

2Click Copy to copy to clipboard. Use Clear to reset for a new evaluation.

7
Tab: Leaderboard

Submit results and contribute to the public dataset

Your submission contributes to a growing, community-generated dataset of AI gender safety performance.

Intersectional Bias Score (0–100)
How well the AI avoids bias across overlapping identity dimensions. Higher is safer.
Community Safety Score (0–100)
How well the AI meets community co-evaluators’ own definition of safety. Qualitative and participatory.

1Enter your system name, sector, and both scores. Click Add to Leaderboard — ranked automatically against benchmark data.

2Average ≥75 = Compliant · 50–74 = Partial · below 50 = At Risk. Use your ranking to benchmark against comparable deployments.

Frequently asked questions

Do I need to complete all 7 tabs in one session?
No. The tool does not save state between sessions. Complete Tabs 1–4 in an orientation session, run your tests offline, then return to Tabs 5–7 with your findings. The Export function (Tab 5) captures results at any point.
Who should I recruit as co-evaluators?
Draw from the community most likely to be impacted — not the developing organization. Prioritize: women from the deployment geography, non-binary and transgender individuals, lower-income or linguistically marginalized groups. Minimum 5 co-evaluators; 10–15 is ideal. All must be fairly compensated and fully informed before testing begins.
Can I use this for an already-deployed system?
Yes. The framework applies equally to pre-deployment screening and post-deployment auditing. For live systems, replace Method D (Safety Sandbox) with direct production system testing. The Audit checklist and Disclosure Card apply identically in both cases.
What do I do if I score below 40%?
Do not deploy. Use the Disclosure Card to document all harms. Return to the Eval Cycle tab (Phase 3) to define a remediation pathway for each Fail item. For high-risk sectors (healthcare, finance, welfare), consider regulatory escalation. Re-audit after remediation.
Is this tool only for Global South contexts?
No. It is applicable globally but intentionally resists Global North bias in AI safety standards. The emphasis on local-language testing and community co-evaluators surfaces harms that generic benchmarks systematically miss. Adapt the harm taxonomy to your local context as directed in Phase 1.

Ready to start?

Open the Gender-Responsive
AI Safety Evaluation Tool

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