# Ps Community And Equity Impact Assessment > Checks an AI-enabled public service design for differential impact across community groups before launch: digital divide, accessibility, and disparate-treatment risk. Use before a public-facing AI service goes live, or when a proposal claims a general improvement and hasn't yet been checked group by group. - Skill: `pilot2service/ps-community-and-equity-impact-assessment` (Agent Skill) - Install (CLI): `npx skillmds@latest add pilot2service/ps-community-and-equity-impact-assessment` - Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/ps-community-and-equity-impact-assessment/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - Author: Pilot2Service (https://skillmd.com/u/pilot2service) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pilot2service/ps-community-and-equity-impact-assessment --- # Community and Equity Impact Assessment ## Purpose An AI service can genuinely improve outcomes on average while making things worse for a specific group — slower, harder to access, or systematically less accurate for them. In a private-sector context this is a product-quality problem; in a public sector context it is also a fairness and, often, a legal problem, since public services generally carry an obligation to serve all eligible groups, not just the average user well. This skill is a structured check for that gap, run before launch rather than discovered after. ## Method ### 1. List who is actually eligible or affected — not just who is using the service today Start from the full eligible population, not the current user base — a service that already underserves a group will show that group as a small share of "current users," making the gap invisible if you only look at existing usage data. Ask: who is entitled to or affected by this service, and does that list match who the AI system was designed and tested against? ### 2. Check each of the standard differential-impact categories Work through these explicitly rather than relying on a general sense that "it should be fine for everyone": - **Digital access**: does using this require a device, connectivity, or digital literacy level not everyone in the eligible population has? Is there a non-digital fallback, and is it genuinely equivalent, not a token alternative? - **Language**: does the system perform as well in every language the service is legally or practically expected to support? AI systems (especially language models) frequently perform worse in lower-resource languages — this needs to be tested, not assumed. - **Disability and accessibility**: does the interface and the AI system's outputs meet accessibility standards, and has it been tested with assistive technology, not just checked against a checklist? - **Age**: does the design assume a level of comfort with technology that skews against older (or, in some cases, younger) users? - **Socioeconomic and geographic**: does access depend on factors correlated with income or location (broadband quality, ability to take time off during service hours) in ways the previous, non-AI process didn't? ### 3. Test for algorithmic disparate treatment, not just access Beyond who can reach the system, check whether the AI's outputs themselves differ systematically by group for people in comparable situations — e.g. does an eligibility-scoring or triage system produce different outcomes for otherwise-similar cases that differ only by a protected characteristic or a strong proxy for one (postcode, surname patterns)? This requires disaggregated outcome data, not an aggregate accuracy figure — an aggregate accuracy figure can look excellent while hiding a large gap for a minority subgroup. ### 4. Distinguish a designed trade-off from an unexamined gap Not every group difference is a problem to fix — sometimes a service is deliberately targeted (e.g. at low-income applicants only). The check here is whether a *difference in quality of service delivery* exists for people who are all supposed to be served equally well, and whether it was a deliberate, justified design choice or simply never tested for. Name which one it is explicitly in the assessment output. ### 5. Feed findings into the decision, not just a report Findings from this skill should feed directly into `ps-regulatory-and-ethical-guardrails-for-public-ai` (since a disparate outcome may cross into legal, not just ethical, territory) and into `ps-decision-readiness-and-public-communication` element 3 (trust) — an equity check that was run and passed is itself strong evidence for the decision-readiness case; a check that wasn't run is a visible gap a scrutinizing decision-maker or journalist will likely ask about. ## What this skill does NOT do - Doesn't perform statistical bias testing itself — it structures what to test for and how to read the results; the actual data analysis may need `../../../../data-strategy-and-literacy/skills/data-bias-and-quality-critical-reading/SKILL.md` or a data science function. - Doesn't make the legal determination of unlawful discrimination — see `ps-regulatory-and-ethical-guardrails-for-public-ai` for when to escalate to legal review. - Doesn't replace direct engagement with affected communities — this is a structuring tool for that engagement, not a substitute for it. ## Refinement notes The five differential-impact categories are a general-practice synthesis for public services, not drawn from one named framework. If the owner has direct case experience with a specific equity failure mode in public AI deployments, it belongs here as a concrete, named example. ## Continue from here - Bias/data quality: `../../../../data-strategy-and-literacy/skills/data-bias-and-quality-critical-reading/SKILL.md` - Legal/regulatory escalation: `../ps-regulatory-and-ethical-guardrails-for-public-ai/SKILL.md` - Presenting the finding: `../ps-decision-readiness-and-public-communication/SKILL.md` ## References - `../../references/source-notes.md` --- **Reminder:** frontmatter has only `name` and `description`. Everything else goes into `skills_index.json` (run `scripts/generate_index.py`).