A clinic posts hours, yet patients show up to a locked door. A responsive clinic apologises, posts why it happened, updates hours, and texts people next time. The fix becomes part of the system.
Competent action creates new information. Refusing to hear it is the fastest path to failure.
Tronto defines responsiveness as "observing that response and making judgements about it — whether the care given was sufficient, successful, or complete." For Civic AI, the public question is simple: when a system harms someone, who can contest the result, how fast, and what changes because they spoke?
Definition
- People closest to harm define harm. They get the author pen for evaluations and a real say in whether repair counted.
- A right to reply must change something. A reply that cannot trigger correction, rollback, or compensation is theatre.
- Shared memory matters. Post-incident learnings become tests; tests prevent repeats.
- Time is part of the service. A fast wrong-then-right beats a slow maybe. Reversible defaults matter.
- Care labour is labour. Eval writing, appeals, moderation, and facilitation should be compensated and visible.
Why it matters
Pack 3 checks whether the process ran as promised. Pack 4 checks whether the care actually landed. That is why responsiveness begins with community judgement, not with an engineering pipeline.
Community-authored evaluations, appeals, and repair logs are the public commitment. Some teams later feed those signals into routing or model updates — sometimes called Reinforcement Learning from Community Feedback (RLCF) — but that implementation choice is secondary. The community authors the yardstick first.
What it looks like in practice
- Community-authored evaluations. Affected communities co-write the tests for harm and successful repair.
- Shared eval registry. Use a public, Wikipedia-like registry for tests: anyone can draft, civil society partners review, and labs adopt or explain.
- Clear appeals. Answer urgent cases in 48 hours, standard in 7 days, and complex in 30 days.
- Visible repair. Give each incident a page: what happened, whom it affected, what changed, and which new test now guards against repeat harm.
From ideas to practice
- Expose the appeal button. Every decision should show a one-click route to challenge it, with the clock visible.
- Accept harm drafts. Let people describe harms in plain language; turn them into community-reviewed tests with partners.
- Triage by severity. Highest-severity cases trigger immediate pause or reversible defaults.
- Fix or explain. Publish the remedy or the reason with next steps, on the clock.
- Memorialize. Turn incidents into tests and link them from the contract changelog.
- Check back. Close the loop with the people who appealed and measure trust-under-loss.
Buildable tools
- Appeal API with timers, statuses, and escalation.
- Community feedback pipeline that can feed approved evals into routing, training, or policy signals when appropriate.
- Eval editor that turns plain-language harms into test harnesses.
- Incident tracker for severity, owners, deadlines, and public notes.
- Repair log template for root cause, remedy, and test added.
One case: the flood-bot
- Appeals surge. A language community flags mistranslations in proof rules.
- Local eval. Community partners submit a translation-fidelity eval; the group is compensated from the project's escrow fund, because local cultural knowledge is labour, not free QA. The bot fails the eval; pause triggers; reversible defaults apply.
- System change. Translation uncertainty now routes cases to bilingual human review rather than auto-denial. If the city later updates the model, this new test becomes a release gate.
- Fix. Bilingual reviewers update rules; the new test guards future changes.
- Close the loop. Elena gets a text: "We fixed the error; here is your new decision; and here's how to see what changed." Trust-under-loss ticks up.
What could go wrong
- Appeal maze. Too many steps. Fix: Single button; auto-escalation if SLA breach.
- Eval spam. Low-quality tests flood the system. Fix: Partner moderation; reputation for contributors; merge/duplicate tools.
- Blame storms. People, not processes, get blamed. Fix: Blameless post-mortems; focus on mechanism design.
- Weaponised appeals. Adversaries flood appeals or strategically trigger pauses to disrupt service. Fix: Require authenticated standing (not public identity) for pause triggers; rate-limit by community; preserve priority access for those directly affected.
Interfaces
- From Responsibility (Pack 2): who acts is clear; remedies are wired.
- From Competence (Pack 3): observability and guardrails feed responsiveness; incident loops start here.
- To Attentiveness (Pack 1): new needs discovered through response reshape what we notice — the cycle restarts.
- To Solidarity (Pack 5): public repair culture builds cross-group trust.
- To Symbiosis (Pack 6): responsive agents earn the right to stay local.
Public measure
The headline public measure for Pack 4 is trust-under-loss: after a bad outcome and attempted repair, do affected people report that the system became more trustworthy rather than less? A supporting diagnostic is whether people who lost on the merits still judge the process and its repair as fair enough to accept.
Supporting diagnostics include appeal timeliness, repair completion rates, repeat-incident rates, and whether community-authored evals come from a broad enough range of affected groups.
A closing image: the workshop wall of retired broken parts
Imagine a workshop with a wall full of retired broken parts, each tagged with the story of how it broke, how to avoid future damage, and who fixed it. The wall is not a wall of shame — it is a wall of learning. The shop that hides its breaks will repeat them.