Be Good · Be Fair · Be Kind
1. Purpose
To ensure Able's AI systems and marketplace practices embody our core values: Be Good, Be Fair, Be Kind.
2. AI governance
| Transparency | A plain-English summary of the factors our matching uses is published on this page. Users can request an explanation of any automated recommendation. |
| Contestability | Workers or buyers may trigger human review of a match or decision within 72 hours. |
| Bias monitoring | We monitor matching outcomes for bias on an ongoing basis and will publish headline fairness metrics as the platform scales. |
| Data minimisation | Only shift-relevant attributes — skills, availability and location — are passed into the model. The matching system never sees names, photos, age, gender, ethnicity or any demographic data. |
| Safety & security | Model inputs and outputs are logged and retained for 12 months for incident response; the platform undergoes regular security testing. |
3. Fair-work standards
| Earnings | Minimum rate at or above the statutory National Minimum/Living Wage for the role; workers are paid 100% of agreed hours within 24 hours of shift completion. |
| Autonomy | Workers set their own rates, availability and travel distance; no exclusivity clauses. |
| Transparency | All fees are displayed to both parties before booking; Able's 8% + VAT buyer fee is fixed and published. |
| Representation | A community channel plus regular round-tables with the CEO. |
| Well-being | The system flags sustained high workloads and prompts rest. |
| Dispute resolution | Escalation path: in-platform chat → Able support → independent mediation (CEDR model rules) within 28 days. |
4. Recognition instead of ratings
| No public star ratings | Able does not display 1-to-5 stars, streaks or public reputation scores for workers or buyers. Numerical scoring reinforces bias, causes stress and can entrench discrimination. |
| Recognition and development instead | After each shift both parties are invited to give structured feedback through short prompts ("What went well?" / "What could be improved?"). Feedback becomes recognition, training and development — visible only to the counter-party and Able's Trust & Safety function unless the author chooses to make it public. |
| AI-assisted summarisation, human-reviewed | Where many shifts occur, a natural-language summary helps users surface common themes; a human moderator checks tone and personal-data disclosure before release. Summaries can be contested via our community channel. |
| Use of feedback in matching | Qualitative insights may be converted into a binary "trusted partner" flag (internal only) after manual verification. The flag can increase the weighting of future matches but never blocks access to shifts; any negative pattern triggers human review. |
| Right to challenge | Workers and buyers can request deletion or redaction of feedback they consider unfair or inaccurate, via the dispute path in §3. |
5. Monitoring & reporting
We track key indicators — fill rate, cancellation rate, wage compliance and fairness metrics — and will publish them as the platform scales.
Last updated: 20 August 2026