Risks.
Named risks, not vague hazards: each entry is a specific, recognised AI risk, with the controls that mitigate it, the clauses that cite it and the sources behind it. 169 risks across 16 categories.
Severity by category
A compact map of the whole register before you filter it.
| Category | critical | high | medium | low | Total |
|---|---|---|---|---|---|
| Governance & Accountability | – | 11 | 10 | – | 21 |
| Model Performance & Reliability | – | 7 | 6 | – | 13 |
| Data Protection & Privacy | 2 | 7 | 4 | – | 13 |
| Fairness, Bias & Impact | – | 4 | 5 | – | 9 |
| Conduct & Customer Outcomes | – | 8 | 4 | – | 12 |
| Transparency & Explainability | – | 2 | 4 | – | 6 |
| Human Factors & Workforce | – | 4 | 3 | – | 7 |
| Model & Application Security | – | 12 | 5 | – | 17 |
| Adversarial ML & Model Integrity | 1 | 4 | 3 | – | 8 |
| Agentic & Autonomous Systems | 1 | 10 | 4 | – | 15 |
| Generative Content & Information Integrity | 1 | 2 | 5 | – | 8 |
| Intellectual Property & Legal Liability | – | 5 | 3 | – | 8 |
| Third-Party & Supply Chain | – | 5 | 4 | – | 9 |
| Operational Resilience | – | 7 | 2 | – | 9 |
| Prohibited & High-Risk Practices | 8 | 1 | – | – | 9 |
| Systemic & Societal | – | 2 | 2 | 1 | 5 |
Browse by list, category or framework
28 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-127 | Age-inappropriate AI interaction Children and young people reach AI features that were designed for adults, without safeguards suited to their age. | Conduct & Customer Outcomes | Medium |
| R-040 | AI-driven social scoring An AI system evaluates or classifies people over time on their social behaviour or personal characteristics, and the score leads to detrimental treatment that is out of context or disproportionate. | Prohibited & High-Risk Practices | Critical |
| R-041 | Biometric categorisation of sensitive attributes A biometric categorisation system categorises individuals from their biometric data to deduce or infer race, political opinions, trade union membership, religious or philosophical beliefs, sex life or sexual orientation. | Prohibited & High-Risk Practices | Critical |
| R-022 | Disparate impact monitoring gap Once an AI system is in use, nothing measures whether its outcomes differ between groups, so discrimination that emerges after launch goes undetected. | Fairness, Bias & Impact | High |
| R-125 | Emotional reliance on conversational AI Users form an attachment to, or dependence on, a conversational system in ways that harm their interests or can be exploited. | Conduct & Customer Outcomes | Medium |
| R-115 | Exclusion and inaccessibility An AI-driven channel cannot be used effectively by disabled people, or by others the organisation has a duty to serve, and no equivalent alternative is available. | Fairness, Bias & Impact | Medium |
| R-042 | Exploitation of vulnerable groups by AI An AI system exploits vulnerabilities arising from a person's age, disability or social or economic situation to distort their behaviour in a way that causes, or is likely to cause, significant harm. | Prohibited & High-Risk Practices | Critical |
| R-117 | Failure to recognise vulnerable customers An automated interaction does not detect or respond to signs of vulnerability that a trained person would notice and act on. | Conduct & Customer Outcomes | High |
| R-024 | Inadequate fairness testing before deployment An AI system that affects people is released without testing whether its performance and outcomes differ between groups. | Fairness, Bias & Impact | Medium |
| R-043 | Individual criminal risk profiling An AI system assesses or predicts the risk that an individual will commit a criminal offence, based solely on profiling them or on their personality traits and characteristics. | Prohibited & High-Risk Practices | Critical |
| R-105 | Inference of sensitive attributes A system infers health, beliefs, sexual orientation or other special category information about a person from data that does not state it. | Data Protection & Privacy | High |
| R-161 | Intrusive AI monitoring of workers AI is used to monitor staff activity, communications or behaviour in ways that are disproportionate, opaque or beyond what the stated purpose needs. | Human Factors & Workforce | High |
| R-124 | Manipulative AI-driven design Personalisation, timing and conversational technique steer customers towards choices that serve the organisation over them, without reaching the level the law prohibits. | Conduct & Customer Outcomes | Medium |
| R-112 | Mis-specified fairness measurement Fairness is tested with a metric, threshold or grouping that does not match the legal test or the harm, so a system passes while remaining unfair. | Fairness, Bias & Impact | Medium |
| R-009 | Misinformation and confabulation A generative model states false or fabricated information with the same fluency and confidence as accurate information, and the output is relied on. | Generative Content & Information Integrity | High |
| R-118 | Misleading AI-generated communications Customer communications or promotions written by AI are unclear, inaccurate or unbalanced, or leave out information the customer needs or the rules require. | Conduct & Customer Outcomes | High |
| R-113 | Missing data to test for bias The organisation does not hold, or believes it may not use, the data on protected characteristics needed to detect discrimination, so bias goes unmeasured. | Fairness, Bias & Impact | Medium |
| R-116 | Poor customer outcomes from AI-driven journeys An AI-driven customer journey causes foreseeable harm to retail customers, or fails to support their understanding and needs, and outcome monitoring does not detect it. | Conduct & Customer Outcomes | High |
| R-028 | Proxy discrimination via correlated features A model produces discriminatory outcomes through features that correlate with a protected characteristic, even though the characteristic itself is not an input. | Fairness, Bias & Impact | High |
| R-114 | Stereotyping and demeaning outputs Generated text or images reproduce stereotypes about groups, or portray them in demeaning ways. | Fairness, Bias & Impact | Medium |
| R-045 | Subliminal or manipulative AI techniques An AI system uses subliminal, purposefully manipulative or deceptive techniques that materially distort a person's behaviour and cause, or are likely to cause, significant harm. | Prohibited & High-Risk Practices | Critical |
| R-005 | Training data bias and discriminatory outcomes Bias in the data a model learns from, whether from history, sampling or labelling, is reproduced or amplified in its outputs and in the decisions that rely on them. | Fairness, Bias & Impact | High |
| R-111 | Unequal performance across groups A system is less accurate or less useful for some groups, languages, accents or dialects than for others, although everyone is treated by the same process. | Fairness, Bias & Impact | High |
| R-162 | Unfair algorithmic management AI is used in recruitment, task allocation, performance assessment or dismissal without fairness testing, explanation, human review or a route to challenge. | Human Factors & Workforce | High |
| R-122 | Unfair personalised pricing AI-driven pricing or offers charge some customers more for reasons unrelated to cost or risk, by exploiting inertia, urgency or personal characteristics. | Conduct & Customer Outcomes | High |
| R-119 | Unsuitable AI-generated advice or guidance A system gives personalised recommendations in a regulated domain without the suitability process, permissions or accuracy that advice requires. | Conduct & Customer Outcomes | High |
| R-047 | Workplace and education emotion recognition An AI system infers the emotions of people at work or in education, for a purpose other than medical or safety reasons. | Prohibited & High-Risk Practices | High |
| R-120 | Wrongful automated refusal A system incorrectly declines, blocks or withdraws a product, payment or claim, and the person affected has no effective way to have it corrected. | Conduct & Customer Outcomes | High |