Expected or possible harm; spans quantitative risk modeling, AI Act risk tiers, clinical risk, and audit risk.
In clinical use of prediction tools, risk is a patient-level probability of a defined adverse outcome within a stated time horizon — a 10-year cardiovascular risk of 12%, a sepsis alert above threshold — that informs but does not replace clinical judgment. Clinicians work with risk as an actionable number attached to a decision threshold (start a statin, escalate observation), read against the individual patient: comorbidities, preferences, and whether the tool's derivation population resembles this patient. A score is a prompt to a conversation about absolute risk, benefit, and harm, not a diagnosis.
Interpret a risk score as an absolute probability for a defined outcome and horizon, check its applicability to the patient in front of you, and communicate it in shared decision-making.
NHS practice uses QRISK3 10-year cardiovascular risk; NICE guidance recommends discussing statin therapy at scores of 10% or above, with the score opening, not closing, the clinical conversation.
OmniGloss seed synthesis, 2026 (machine-drafted, pending expert validation)
A. Alba et al. (2017). Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature. JAMA
For hospital leadership authorizing an AI tool, risk is the regulatory-and-safety exposure that determines what may be deployed under which controls: the device's regulatory risk class (from wellness software to autonomous diagnostic), the severity of the clinical situation it touches, and the institution's residual liability if it errs. Adoption decisions weigh a documented benefit-risk determination — evidence of clinical benefit against foreseeable harms and use errors — plus insurer, MDR/FDA, and credentialing implications. A tool is acceptable when its residual risk is justified by benefit and covered by monitoring, training, and rollback arrangements.
Evaluate an AI tool's regulatory risk class and benefit-risk evidence, and authorize deployment only with monitoring, training, and rollback provisions that match that class.
IDx-DR, the first FDA-authorized autonomous AI diagnostic (2018, diabetic retinopathy screening), was cleared with a tightly defined intended use and exclusion criteria — the kind of risk-bounded authorization adoption committees look for.
FDA, Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan (2021)
risk categorization of SaMD; total product lifecycle approach
For developers of clinical AI regulated as a medical device, risk is an engineered quantity managed through a hazard-analysis workflow: enumerate hazards and hazardous situations (missed alert, wrong dose suggestion, automation-induced complacency), estimate probability of occurrence and severity of harm for each, implement risk controls in priority order (inherent safe design, protective measures, information for safety), and verify that residual risk for every hazard, and in aggregate, meets pre-set acceptability criteria recorded in the risk management file. For adaptive models, risk estimates are revisited at every retraining and monitored post-market.
Maintain a hazard-by-hazard risk analysis for the AI function, implement and verify risk controls, and re-estimate residual risk whenever the model, data, or intended use changes.
e.g., an insulin-dose advisor whose hazard analysis rates a suppressed hypoglycemia warning as low-probability but catastrophic-severity, forcing a hard alarm that by design cannot be silenced.
Art 9 (risk management system: identify, estimate, evaluate, and mitigate risks throughout the lifecycle)
For clinical prediction modelers, risk is the model's output itself: a calibrated probability that a patient experiences a defined endpoint within a defined horizon, estimated from cohort data. It is operationalized through the modeling contract — endpoint definition, prediction horizon, eligible population — and judged by discrimination (can the model rank patients), calibration (do predicted probabilities match observed frequencies), and net benefit at the intended decision threshold. A risk model that is miscalibrated in the deployment population is wrong even if its ranking is good, because downstream treatment thresholds consume the absolute number.
Specify endpoint, horizon, and eligible population; report discrimination, calibration, and net benefit in the deployment population before any threshold is wired to clinical action.
External validation of the Epic Sepsis Model (Wong et al. 2021) found materially weaker discrimination and calibration than originally reported, showing why deployment-population evaluation is part of what risk means here.
Anchors the 'risk as calibrated quantity' position in the recorded dissensus over whether risk is an aggregate quantitative measure or an unaggregatable burden borne by particular people.
In clinical-AI assurance work, risk is the documented combination of probability and severity of harm that remains after controls — the residual risk whose acceptability the organization must be able to defend to regulators, ethics committees, and courts. Auditors operationalize it through the risk management file: is every reasonably foreseeable hazard, including foreseeable misuse and automation bias, identified; are estimates evidence-based; were controls verified; is residual risk explicitly accepted by an accountable owner against pre-defined criteria; does post-market surveillance feed incidents back into the analysis? Risk here is exclusively harm to patients — benefit is a separate determination, never netted against harm.
Assess whether hazard identification is complete, residual-risk acceptance is documented and owned, and post-market incidents demonstrably re-enter the risk analysis.
e.g., an audit finding that a deployed deterioration-alert model had no documented owner for accepting its false-negative residual risk, and no route from incident reports back into the hazard analysis.
Holds the harm-only position in the recorded scope dissensus: in this community risk is only ever adverse, in contrast with uncertainty-on-objectives framings that include upside.
For front-line credit and underwriting staff, risk arrives as a score: a probability of default, a fraud propensity, an underwriting grade produced by models they did not build. Working with it means knowing the score's meaning (which outcome, which horizon, which population), the cutoff and override policy that translates it into decisions, and the documentation duty when deviating from it. A score is bank policy made numeric — staff must apply it consistently, recognize inputs that look wrong, and route disputed or borderline cases to review rather than improvising, because their handling of the number is itself examinable in audits and adverse-action processes.
Apply model scores under the institution's cutoff and override policy, verify inputs look plausible, document any deviation, and route disputes to review rather than adjusting scores informally.
The CJEU's SCHUFA judgment (C-634/21, 2023) treated credit-score creation as automated decision-making under GDPR Art. 22, sharpening what score users must be able to explain to affected customers.
Art 22 (automated individual decision-making, including profiling)
For bank executives and board risk committees, risk is exposure taken deliberately in pursuit of return, managed against a written risk appetite: how much credit, market, and operational exposure the institution will accept, expressed in limits, capital, and stress outcomes. AI enters as another exposure class to be sized and accepted — model limitations, third-party dependence, conduct risk — not as something to be eliminated. A decision here is an act of risk acceptance by a named owner within delegated limits; consequences can be favorable or unfavorable, and forgoing an opportunity is itself a risk decision recorded against objectives.
Size an AI-related exposure against the institution's risk appetite, accept or escalate it as a named owner within delegated limits, and record the acceptance rationale.
e.g., a board accepting a documented model-risk exposure to extend credit to thin-file customers, judging the expected return and inclusion gains against a quantified loss tolerance.
Holds the upside-inclusive position in the recorded scope dissensus: risk here is uncertainty on objectives with two-sided consequences, against regimes that define risk as harm only.
NIST AI Risk Management Framework 1.0 (2023)
§1.1 (risk as composite of probability and magnitude; consequences can be positive, negative, or both; adapted from ISO 31000:2018)
NIST AI 100-3, The Language of Trustworthy AI: An In-Depth Glossary
entry 'risk' (ISO/IEC 38507: 'effect of uncertainty on objectives')
For quantitative developers, risk is the modeled quantity: a probability of default over twelve months, a loss-given-default, a 99th-percentile value-at-risk over a ten-day horizon. Operationalizing it means fixing the measurement contract — default definition, horizon, confidence level, data window — then estimating, backtesting, and documenting the number so that capital, pricing, and limits can consume it. The measure's authority comes from its testability: breaches of a VaR band or drift in realized default rates against predicted PDs are objective evidence that the operationalization is failing, triggering recalibration or model change.
Define the risk measure's contract (outcome, horizon, confidence, population), estimate and backtest it against realized outcomes, and document performance so downstream capital and pricing users can rely on it.
Under Basel internal-ratings-based approaches, banks estimate PD, LGD, and EAD per exposure, and supervisory backtesting compares predicted default rates with realized ones.
Anchors the 'risk as calibrated quantity' position in the recorded dissensus: risk becomes actionable only once fixed as a testable number.
Federal Reserve SR Letter 11-7 / OCC 2011-12, Supervisory Guidance on Model Risk Management (2011)
definition of model; model development and testing expectations
In day-to-day model development under model-risk management, risk also names the model's own capacity to cause loss: the potential for adverse consequences from decisions based on incorrect or misused model outputs. Builders operationalize it procedurally — document assumptions and limitations, test against out-of-sample and stressed conditions, register the model in the inventory with a risk tier, and hand validation an evidence pack — because a model without this trail is unusable regardless of its accuracy. The everyday question 'what is the model risk here?' asks about error, misuse, and misapplication, not about the portfolio the model measures.
Document assumptions, limitations, and intended use; test outside the development sample; register the model with a risk tier; and supply validation with evidence before use.
e.g., a bank's governance debate over whether a vendor LLM assistant is a 'model' under SR 11-7 at all, and hence whether it needs tiering and validation before customer-facing use.
Live variance over scope: whether ML and generative systems (and spreadsheets or rules engines) count as 'models' under model-risk definitions divides MRM functions, with material consequences for validation workload and deployment speed.
definition of model risk (adverse consequences from decisions based on incorrect or misused model outputs)
For model validation and internal audit, risk is what the model-risk-management apparatus exists to bound: each model's tier in the inventory (materiality, complexity, reliance), the depth of independent validation that tier commands, and the aggregate model-risk profile reported to the board. Operationally, risk lives in the machinery — a complete inventory, effective challenge performed by qualified independent staff, findings tracked to closure, periodic revalidation triggered by time or change. A model used outside its validated scope, or a finding aging past its deadline, is risk in this register, whatever the model's statistical performance.
Tier every model by materiality and complexity, perform independent effective challenge proportional to tier, track findings to closure, and report the aggregate model-risk profile upward.
JPMorgan's 2012 'London Whale' losses involved a CIO value-at-risk model with flawed implementation and weak review — a canonical model-risk-management failure examined in a U.S. Senate investigation.
validation, effective challenge, and governance expectations
For working journalists and creators using generative tools, risk is what can go wrong between draft and publication: fabricated facts and quotes, uncleared third-party material surfacing in output, undisclosed AI assistance breaching outlet policy, and a byline's credibility spent on an error. It is managed as craft practice, not paperwork: verify every checkable claim against a primary source, treat model output as an unvetted tip, know the outlet's disclosure rules, and keep prompts and drafts as a record of one's own contribution. The unit of loss is trust — of editors, sources, and audience — which does not recover at the speed it is lost.
Treat generative output as unverified source material: check claims against primary sources, clear rights, follow disclosure policy, and keep records of your own contribution.
CNET's 2023 publication of AI-drafted finance explainers ended in corrections across dozens of articles and a pause of the program — a newsroom-policy reference point since.
Among working creators, risk also names what generative systems do to livelihoods and creative identity: training on their work without consent or payment, style imitation that competes with them in their own market, commission volumes collapsing at the entry level where careers begin, and credit disappearing into unattributed synthesis. This risk is operationalized collectively rather than probabilistically — through contract clauses on AI training and reuse, union bargaining positions, opt-out registries, and disclosure norms — because for a working artist the relevant question is not the expected loss across the sector but whether their own practice remains viable.
Identify where AI training, imitation, or substitution touches your practice; secure contract and licensing terms on training and reuse; and use collective instruments where individual bargaining fails.
The 2023 WGA and SAG-AFTRA agreements set terms on AI use of writers' material and performers' digital replicas — livelihood risk negotiated into contract language.
Anchors the 'risk as lived, distributional burden' position in the recorded dissensus: sector-level expected-loss figures do not answer whether an individual practice survives.
For editors, producers, and publishers greenlighting AI use, risk is legal and reputational exposure attached to output they choose to publish: copyright claims over training data or substantially similar output, likeness and defamation liability for synthetic depictions, disclosure obligations for AI-generated content, and audience-trust damage that outlasts any correction. It is operationalized as a pre-publication assessment: what is the provenance of this material, which rights are cleared, does policy require labeling, who signs off, and would we defend this use in public? Insurance, vendor indemnities, and precedent-watching are part of the calculus.
Before authorizing AI-assisted content, assess rights provenance, likeness and defamation exposure, and disclosure duties; assign sign-off; and secure vendor indemnities where exposure remains.
Sports Illustrated's 2023 publication of articles under fabricated AI-generated author personas triggered audience backlash and removal of the content — reputational risk realized faster than any legal process.
Art 50 (transparency obligations: disclosure and marking of AI-generated and manipulated content)
For teams building generative features into media and games products, risk is a testable inventory of failure modes: the model reproducing copyrighted or trademarked material, generating a real person's likeness or voice, emitting unsafe or off-brand content, and leaking prompts or user data. It is operationalized through red-teaming against that inventory before launch, automated output filters and similarity checks, provenance marking of synthetic content, and telemetry that surfaces novel failures in production. Acceptance is threshold-based — measured failure rates per category under adversarial prompting — and each release re-runs the suite, because model updates redistribute failure probability.
Maintain a failure-mode inventory, red-team each release against it, enforce output filtering and provenance marking, and monitor production for failure modes the suite missed.
e.g., a game studio red-teams its dialogue model with adversarial prompts seeking protected character likenesses and real-person voices, blocking release until measured failure rates fall below policy thresholds.
For standards editors, rights-clearance teams, and platform trust functions, risk is unresolved status: content whose rights chain, consent basis, or AI involvement cannot be evidenced at the moment someone asks. The operational form is a clearance record — what was used to make this, under which license or consent, with what AI contribution, disclosed how — kept audit-ready against complaints, takedown demands, and litigation discovery. A work is risky not because harm is probable but because its documentation cannot answer a challenge; stewardship converts diffuse exposure into a checklist whose gaps are findings assigned for remediation.
Verify and archive the rights chain, consent basis, and AI-contribution record for published work; treat any undocumented element as a finding requiring remediation.
Getty Images' litigation against Stability AI over training-data use illustrates the exposure clearance functions must anticipate: claims that reach back through a work's inputs, not just its published surface.
For caseworkers handling files with algorithmic risk scores — fraud likelihood, non-compliance flags, prioritization ranks — risk is a triage signal that must be converted into lawful individual assessment before it touches a citizen. Working with it means knowing what the score claims to predict, treating it as one input requiring corroborating case-specific evidence, recording reasons in reviewable form, and never presenting 'the system flagged you' as grounds for a decision. Score-driven attention is itself an intervention — who gets inspected, delayed, or asked for more documents — so a caseworker's handling of a score is where due process is kept or lost.
Use a risk score only to prioritize attention, ground any adverse step in case-specific evidence, record reviewable reasons, and escalate scores that appear systematically wrong.
In State v. Loomis (Wisconsin, 2016), the court permitted COMPAS recidivism scores at sentencing only with warnings that a score may inform but never determine the outcome — the triage-not-determination line drawn in case law.
Offices vary on the score's evidentiary status: some treat it purely as a workload-triage aid, others allow it as a contributing ground for adverse action — a live divergence in administrative practice and case law.
For agency heads and procurement authorities, risk under the AI Act is a legal classification that determines obligations before any statistical estimate exists: the combination of the probability of an occurrence of harm and the severity of that harm, with a system's placement — prohibited practice, Annex III high-risk, limited, minimal — following from its intended purpose in an administrative context. Deciding to deploy means deciding the classification, since high-risk status carries conformity assessment, registration, human oversight, logging, and fundamental-rights impact assessment duties. Risk here is a compliance category with attached duties, exclusively about harm to health, safety, and fundamental rights.
Classify each contemplated system against the AI Act's risk tiers by intended purpose, and authorize deployment only with the obligations of that tier resourced and evidenced.
AI systems used by public authorities to evaluate eligibility for essential public benefits fall under Annex III high-risk, committing the deploying agency to oversight, logging, and impact-assessment duties.
Holds the harm-only position in the recorded scope dissensus: the Act's definition admits no upside component, unlike uncertainty-on-objectives framings used in enterprise risk management.
Art 6 and Annex III (classification rules for high-risk AI systems)
Martin Ebers (2024). Truly Risk-based Regulation of Artificial Intelligence: How to Implement the EU's AI Act. European Journal of Risk Regulation
Isabel Kusche (2024). Possible harms of artificial intelligence and the EU AI act: fundamental rights and risk. Journal of Risk Research
For data scientists building risk-scoring and prioritization models inside government, risk is a predicted quantity whose error structure is an administrative act: false positives subject citizens to inspection or delay, false negatives leave duties unmet, and both distribute unevenly across groups the state must treat equally. Operationalizing it means defining the target without proxy drift (fraud found is not fraud committed), measuring error rates and calibration per subgroup, testing for feedback loops where enforcement data trains future targeting, and shipping the model with thresholds, override routes, and monitoring specified — because in administration the error budget is a policy choice, not a technical residue.
Define targets to avoid enforcement-proxy drift, report subgroup error rates and calibration, test for feedback loops, and specify thresholds and override routes as part of delivery.
The UK's 2020 Ofqual grading algorithm assigned exam grades from historical school performance; systematic downgrading patterns caused public failure and withdrawal within days — error structure experienced as injustice.
In official statistics, risk means disclosure risk: the probability that a released table, microdata file, or model output allows a unit — person, household, business — to be re-identified or an attribute inferred, in breach of statistical confidentiality. It is operationalized in the release pipeline: minimum cell counts and dominance rules, perturbation or suppression applied before publication, penetration testing of anonymized microdata against plausible linkable sources, and access tiers from public files to secure research environments. The standard is scenario-based — what could a motivated intruder with named auxiliary data achieve — and it gates every release, because confidentiality underwrites respondents' willingness to report.
Assess each release against intruder scenarios with plausible auxiliary data, apply disclosure controls before publication, and match access arrangements to residual re-identification risk.
The Netflix Prize de-anonymization (Narayanan and Shmatikov 2008) showed sparse behavioral microdata can be re-identified by linkage — the class of scenario disclosure-control testing must anticipate.
European Statistics Code of Practice (2017 revision)
Principle 5 (Statistical confidentiality and data protection)
Article 29 Working Party, Opinion 05/2014 on Anonymisation Techniques (WP216)
re-identification criteria: singling out, linkability, inference
For ombudsmen, audit institutions, and rights-impact assessors, risk is the prospect of a state system harming citizens' rights — and its assessment is distributional before it is probabilistic. The operating questions: which groups bear the system's errors and burdens; is any individual harm (wrongful benefit denial, wrongful suspicion) severe enough that no aggregate benefit offsets it; can affected people know, contest, and be made whole? Instruments are the fundamental-rights impact assessment and DPIA, complaint-pattern analysis, and inspection of contestation routes. A system whose expected loss is small but whose failures concentrate on the already-vulnerable is high-risk in this register.
Assess who bears a system's errors and burdens, whether severe individual harms are treated as offsettable, and whether affected people can know, contest, and obtain redress.
The Dutch childcare-benefits scandal: aggregate fraud-prevention gains coexisted with concentrated, severe harm to specific families — precisely the distribution an aggregate risk figure conceals.
Anchors the 'risk as lived, distributional burden' position in the recorded dissensus: severity to rights resists expected-value aggregation and is assessed per affected group and person.
Art 27 (fundamental rights impact assessment for high-risk AI systems)
Art 35 (data protection impact assessment where processing is likely to result in a high risk to rights and freedoms)
For regulators and AI-office policymakers overseeing general-purpose models, risk scales with reach: systemic risk attaches to the high-impact capabilities of general-purpose AI models — harms that can propagate at scale across the value chain to public health, safety, security, fundamental rights, or society as a whole, whether actual or reasonably foreseeable. It is operationalized through designation: capability thresholds such as training-compute presumptions, obligations that follow designation — model evaluation, adversarial testing, incident reporting, cybersecurity — and standing machinery to reassess as capabilities move. The unit of analysis is not one deployment but a model's entire downstream footprint.
Apply designation criteria to general-purpose models, impose and verify evaluation, adversarial-testing, and incident-reporting obligations on designated models, and reassess as capability evidence changes.
Under the AI Act, general-purpose models trained with more than 10^25 floating-point operations are presumed to present systemic risk, triggering evaluation and reporting duties for their providers.
Art 3(65); Arts 51-55 (classification and obligations for general-purpose AI models with systemic risk)
For commissioning editors, producers, and studio executives deciding what gets made, risk is the stake placed on uncertain audience response: a slate is a portfolio in which most titles underperform and a few hits carry the rest, so 'taking a risk' on an unproven format, voice, or tool is the job, not a failure of control. Generative AI is weighed on both sides of that ledger — cost and speed gains and new formats against brand damage and rights exposure. The competent act is sizing the bet: development spend, release positioning, and kill criteria set in advance, not the elimination of downside; a slate with no failures signals too little risk taken.
Size a creative bet against the slate's loss tolerance, set development spend and kill criteria before commissioning, and treat AI adoption as exposure taken deliberately for identified upside.
e.g., a streamer greenlights an AI-assisted animation pilot with a capped budget and pre-agreed kill criteria, accepting a defined write-off in exchange for a shot at a new format.
Second anchor of the upside-inclusive position in the recorded scope dissensus: in commissioning practice risk is deliberately taken for reward, not only minimized — the same executives run a separate harm-only clearance process before publication.
In farming and environmental management, risk is the probability and severity of biophysical and financial harm on a seasonal clock: frost after budburst, drought at grain fill, pest outbreaks, flood, and the penalty exposure of non-compliance. Data and AI reframe it into index triggers and early warnings — a rainfall index that pays out, a pest model that times spraying — and add a new layer: the risk of committing irreversible in-season decisions to a model output that cannot be corrected after harvest.
Quantify the probability and severity of seasonal harms, set index and warning thresholds accordingly, and assess separately the added risk of acting irreversibly on model outputs.
e.g., index-based drought insurance pays on a rainfall index rather than assessed losses; the operative figure is basis risk — the probability a farmer suffers real loss while the index stays silent.
risk as composite measure of an event's probability and the magnitude of its consequences
Military decision-making operationalizes risk as a commanded quantity: staffs assess probability and severity of harm on standard matrices, distinguish risk to force, meaning own casualties and equipment, from risk to mission, meaning failure of the operation, and present residual risk to a commander at the echelon authorized to accept it. Acceptance is an explicit, recorded command act; risk is never merely noted, it is owned. AI-enabled systems enter this grammar as contributors of new hazard classes, such as misclassification, brittleness, and adversarial manipulation, whose residual risk must be characterized by test authorities and formally accepted by an accountable commander before fielding or engagement.
Assess probability and severity of harm, separate risk to force from risk to mission, and route residual risk to the command echelon authorized to accept it, in writing.
E.g., a brigade commander accepts moderate residual risk of misclassification by a counter-drone system in a defended sector, documented in orders, after reviewing its tested false-engagement rate.
risk as composite of event probability and consequence magnitude
In everyday educational practice, risk attaches to the learner before the system: an 'at-risk' student is one whose attendance, grades, or circumstances predict failure or dropout, and early-warning indicators exist to trigger support, not sanction. With the AI Act, a second usage lands in the same institutions: education is a listed high-risk domain, so admissions, assessment, and proctoring systems themselves become 'high-risk AI' carrying conformity obligations. Practitioners now operationalize both at once — thresholds and referral protocols for learner risk, and classification, documentation, and oversight duties for system risk — and must keep the two ledgers distinct.
Distinguish learner risk from system risk: set evidence-based thresholds and support pathways for the first, and classify educational AI against regulatory high-risk categories for the second.
e.g., a university early-warning dashboard flags first-year students for tutoring outreach, while the same institution must separately assess whether the dashboard itself is a high-risk AI system under the EU AI Act's education annex.
What 'risk' denotes varies within institutions: student-support staff mean the probability a learner fails without intervention, while compliance teams mean the regulatory classification of the tool; conflating the two turns welfare instruments into compliance artifacts and vice versa.
Art. 3(2); Annex III(3) (education and vocational training)
Engineering runs on formalized risk arithmetic: FMEA rates severity, occurrence, and detection for each failure mode; machine-safety risk assessment combines probability and severity of harm to size guarding and safety functions; functional-safety analysis allocates integrity levels to the protections. When AI enters a machine, it slots into the same machinery rather than replacing it — a vision-based safety component gets a hazard analysis, its failure modes (missed detection, false trip) get rated, and the AI Act's probability-times-severity definition reads to a machine-safety engineer as a restatement of what the risk assessment file already does.
Enter AI failure modes into the existing FMEA and machine-safety risk assessment, rate severity, occurrence, and detection honestly, and size mitigations to the rated risk.
e.g., a cell's risk assessment rates 'AI presence detector misses a person' as high severity and adds a redundant light curtain, exactly as it would for any single-channel sensor.
In legal counselling, risk is what the client is advised to accept, mitigate, insure, or restructure around: the probability of an adverse finding combined with the exposure it carries, communicated in opinion language whose qualifiers — 'more likely than not', 'reasonable basis' — bear defined professional weight, and kept under privilege so the assessment itself is protected. AI regulation adds a classification exercise: determining whether a client's system is prohibited, high-risk, or limited-risk under the AI Act, where the tier, not a quantitative estimate, determines the obligations. Litigation risk is likewise operationalized in reserves and settlement ranges.
State legal risk in calibrated opinion language, tie each assessment to the exposure and probability that ground it, and classify AI systems into the regulatory tier that fixes the client's obligations.
e.g., counsel classifying a client's CV-screening tool as high-risk under the AI Act triggers conformity-assessment, logging, and oversight obligations — the classification, not a risk score, is the operative act.
In transport planning, risk is the chance and cost of the network failing freight: probability-weighted exposure to delay, loss, damage, theft, customs inspection or seizure, and accident, assessed per lane, mode, and cargo class. Planners score corridors by disruption likelihood and buffer schedules accordingly, insurers price cargo risk, and dangerous-goods rules impose categorical handling regardless of computed probability. A high-risk shipment means one needing extra buffer, security, or documentation, a usage that predates and now coexists with AI-regulation risk tiers arriving via autonomous and safety-relevant systems.
Score lanes and shipments by likelihood and severity of delay, loss, and inspection; set buffers, routing, and insurance accordingly; and re-score when disruption intelligence changes.
e.g., rerouting high-value electronics off a corridor after a spike in cargo-theft reports, accepting two days of added transit time as the price of reduced loss exposure.
risk: composite measure of an event's probability and the magnitude of its consequences (adapted from ISO 31000:2018)
F. Goerlandt et al. (2015). Maritime transportation risk analysis: Review and analysis in light of some foundational issues. Reliab. Eng. Syst. Saf.
On a shift floor or a care round, risk is managed as concrete harm scenarios with owners and controls: a fall during a solo home visit, an allergen reaching the wrong plate, an intoxicated guest, a lone worker sent to an unvetted address by an app. Data and AI systems enter this register as new risk carriers — a rostering model that erodes rest breaks, a dispatch system routing workers into unsafe areas at night — and are assessed the way inspections always have: likelihood of the harm, severity if it lands, and the mitigation on file.
Assess data-driven scheduling, dispatch, and monitoring systems within existing safety routines: name the harm, estimate likelihood and severity, assign a mitigation, and record it for inspection.
e.g., a risk assessment for a lone-worker app rates GPS check-in failure during a night visit as low-likelihood, high-severity and mandates a phone-call fallback protocol.
In marketing operations, risk is what can burn budget or brand before anyone notices: media spend leaking to fraudulent or unviewable inventory, ads served against unsafe content, a personalization stack running on invalid consent and accruing regulatory exposure, deliverability collapse after a spam-trap hit, and the reputational tail of a tone-deaf automated campaign. It is operationalized through brand-safety adjacency controls and blocklists, fraud-verification vendors, consent-coverage monitoring, and campaign-level sign-off matrices that weigh expected revenue against these exposures — a portfolio of guardrails rather than a single quantified score.
Maintain brand-safety and fraud controls on every media buy, monitor consent coverage as regulatory exposure, and require documented sign-off weighing revenue against reputational and compliance downside for automated campaigns.
In 2017, major brands pulled YouTube spend after their ads ran alongside extremist videos — an adjacency risk that verification vendors and placement blocklists were subsequently built into media buys to control.
In research governance, risk is what an ethics committee weighs before a study may proceed: foreseeable harms to participants and to third parties, whether physical, psychological, social, legal, or informational, appraised for probability and severity against the study's expected knowledge gain, with mitigations, monitoring arrangements, and stopping rules written into the protocol. It also covers harms a study creates beyond its participants, such as re-identification of a small community, dual-use of methods or data, and environmental cost. Researchers operationalize it as an approvable protocol section with named mitigations, not as a number, and re-assess it by amendment whenever the design changes.
Enumerate foreseeable harms to participants and third parties in the protocol, state proportionate mitigations and stopping rules, and file an amendment whenever a design change alters the risk profile.
e.g., a study of a small stigmatized population where the principal risk is not the intervention but publication of subgroup tables detailed enough to identify individuals, mitigated by aggregation and an embargo.
Regulation (EU) 2024/1689 (AI Act), definition of risk
Art. 3(2) (combination of the probability of an occurrence of harm and the severity of that harm)
In engineering practice, risk is what the launch process prices: probability-times-impact judgments encoded in threat models, pre-mortems, error budgets, and go/no-go reviews. Teams operationalize it as budgets and tiers - an SLO's error budget authorizes a quantified amount of failure; a risk register assigns owners and mitigations; an AI feature's tier decides which extra gates apply. Under the AI Act the same word also assigns the provider regulatory obligations by risk class, so builder risk practice now runs on two ledgers: reliability risk to the service and classified risk to persons.
Run risk assessment as a release gate: classify the feature, quantify likelihood and impact, assign mitigations to owners, and spend error budget deliberately rather than by accident.
e.g., an LLM support-bot feature is tiered as customer-facing high-impact, which adds red-team evaluations and a human-escalation path to its launch checklist, while its serving SLO burns error budget like any other service.