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An AI project qualifies as social good only when it produces a legitimate public benefit without imposing disproportionate risks on the people and communities affected. Assess it as a lifecycle and a social system: define the benefit and baseline, test whether AI is necessary, map who gains and who may be harmed, evaluate safety, fairness, privacy and security, preserve meaningful human control, and monitor results after launch.
A credible assessment also asks what happens when conditions change. Benefits can be uneven, local institutions may be unprepared, environmental and labor effects can accumulate, and dependence on a single supplier can reduce public autonomy. The sections below provide a practical assessment sequence, evidence requirements, governance roles, comparison criteria and a long-term monitoring plan.
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What an AI-for-social-good assessment must establish
The central question is not whether a model is accurate in a benchmark. It is whether the complete system improves a defined social outcome, for the people it is meant to serve, under real operating conditions, without creating unacceptable rights, safety or power risks.
Start with four tests:
- Legitimacy: Is the objective a genuine public or social need, and is the organization authorized to pursue it?
- Necessity: Is AI needed, or could a simpler process achieve the same result with less risk?
- Proportionality: Are the expected benefits sufficient to justify the intrusiveness, uncertainty and possible harms?
- Accountability: Can a responsible person explain, challenge, override, repair or stop the system?
UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021 and applicable to all 194 Member States, states: “The use of AI systems must not go beyond what is necessary to achieve a legitimate aim.” That principle makes the use case, not the technology, the starting point.
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Step 1: Define the public-value claim
Write the benefit in operational terms
Describe the intended outcome in one sentence, identify who should receive it, and specify the baseline against which improvement will be measured. For example, a public-health project might aim to reduce the time to identify patients who need follow-up, while preserving clinician judgment and equal access. A vague goal such as “modernize services with AI” cannot support a meaningful impact assessment.
Record the decisions the system will influence, the people who remain responsible, and the limits of the system’s authority. Distinguish assistance from automated determination. A tool that prioritizes a queue has a different risk profile from one that denies a benefit or controls access to essential services.
Define failure before deployment
Set measurable failure conditions alongside success measures. Include unacceptable error rates, subgroup disparities, privacy breaches, unsafe recommendations, missed appeals, service outages and evidence that users are being excluded. State who can accept a residual risk and what evidence they must review.
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Apply a necessity and proportionality gate
Compare the proposed AI system with non-AI alternatives, including additional staff, rules-based software, better data collection or a smaller pilot. If the same legitimate aim can be reached with less data, less automation or less exposure to irreversible harm, the lower-risk option should be preferred. A social label does not exempt a project from this test.
Step 2: Map affected people, institutions and environments
List direct and indirect stakeholders
Build a map that includes more than the people who operate the interface. Include:
- People who receive recommendations, scores or decisions.
- Direct users, such as clinicians, teachers, caseworkers or farmers.
- Bystanders whose data may be captured or inferred.
- Workers whose tasks, evaluation or job security may change.
- Children, older people, people with disabilities and marginalized or vulnerable groups.
- Communities, local organizations and public institutions that must absorb consequences.
- People without smartphones, reliable connectivity, digital literacy or the dominant language.
- The environment, including energy, hardware, water and disposal impacts where relevant.
Identify power and appeal asymmetries
For each group, ask who controls data, who sets the rules, who can refuse participation, who bears the cost of an error and who can appeal. A person subject to an automated recommendation may have less information and fewer resources than the agency deploying it. Record whether an appeal is available before harm occurs, how quickly it is handled and whether the reviewer has authority to change the outcome.
Trace the wider system
Map upstream data suppliers, model providers, integrators, contractors and downstream decision makers. Note dependencies on telecommunications, cloud infrastructure, identity systems and public registries. A technically sound model can still fail if a partner changes an input definition, an institution lacks staff to review outputs or a community cannot access the service.
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Step 3: Assess safety and security
Safety concerns unintended failures; security concerns deliberate attacks and unauthorized access. Assess both throughout design, deployment, updates and retirement. UNESCO says that unwanted harms and vulnerabilities to attack should be avoided and addressed by AI actors. OECD principles require systems to remain robust, secure and safe throughout their lifecycle and to be overridable, repairable or safely decommissioned when they risk undue harm.
Build a threat and failure register
| Risk area | Questions to answer | Evidence or control |
|---|---|---|
| Distribution shift | What happens when language, population, season, equipment or policy changes? | Out-of-distribution tests, drift thresholds and a revalidation trigger. |
| Model failure | Which errors are likely, and which could cause severe or irreversible harm? | Scenario testing, subgroup error analysis and a safe fallback process. |
| Misuse | Could an authorized user apply the tool outside its intended purpose? | Use restrictions, training, access controls and misuse monitoring. |
| Adversarial attack | Can inputs, prompts, labels or interfaces be manipulated? | Red-team exercises, authentication, rate limits and incident procedures. |
| Data leakage | Could sensitive records, prompts or model outputs expose personal information? | Minimization, isolation, logging, access reviews and tested deletion. |
| Unsafe automation | Can a recommendation be executed without an informed human check? | Human approval gates, clear confidence limits and an emergency stop. |
| Cascading effects | Could one error propagate across agencies, queues or essential services? | Dependency mapping, rate controls, manual continuity plans and rollback. |
Set severity, reversibility and response thresholds
Classify each failure by likelihood, severity, number of people exposed and reversibility. A wrong low-stakes suggestion may be corrected immediately; an erroneous eligibility decision, medical recommendation or surveillance match may not be. For high-severity or hard-to-reverse outcomes, require real-world testing, independent review and a documented stop condition before expansion.
Test the human factors
Observe how real users interpret confidence scores, warnings and explanations. Test automation bias: people may accept a system’s output even when they have contrary evidence. Check workload, language, accessibility and the time available for review. A nominal human-in-the-loop is not meaningful if the reviewer cannot understand the output or is penalized for overriding it.
Step 4: Test fairness and inclusion
Choose measures that match the harm
There is no single fairness score that fits every use case. Select measures based on the decision and its consequences:
| Dimension | Useful questions | Examples of evidence |
|---|---|---|
| Outcome | Do groups receive beneficial results at comparable rates when their needs are comparable? | Approval, referral, recovery or service-completion rates by group. |
| Error | Are false positives and false negatives distributed acceptably? | False-positive, false-negative, sensitivity and specificity analysis. |
| Access | Can people use the service regardless of device, language, disability or connectivity? | Completion rates, wait times, language coverage and accessible-channel use. |
| Burden | Who must provide extra documents, wait longer or undergo additional review? | Appeal rates, repeat submissions, travel, fees and staff time. |
| Process | Can affected people understand and challenge an output? | Notice delivery, explanation comprehension and appeal resolution. |
Disaggregate responsibly
Analyze results across relevant protected and vulnerable groups, language, disability, geography, age and gender where lawful, meaningful and safe. Explain missing data and avoid forcing people into categories that do not describe them. Small samples may require qualitative review, aggregation or privacy-preserving methods rather than publication of identifiable results.
Check exclusion and digital divides
A system can show equal model accuracy while excluding people who cannot reach it. Test non-digital channels, low-bandwidth operation, assistive technologies, local languages and literacy levels. Ask whether the deployment shifts costs to people who must buy a device, travel to a service point or repeatedly correct a record. UNESCO identifies social justice, fairness, non-discrimination, inclusive access and attention to marginalized and vulnerable people as core requirements.
Investigate proxy discrimination
Remove a sensitive field does not remove its proxies. Geography, language, school, occupation, device type or purchasing history may encode protected characteristics. Review feature provenance, correlations and outcome differences, then decide whether a feature is necessary, restricted or excluded.
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Step 5: Protect privacy, agency and contestability
Document the data lifecycle
For every dataset and input, record provenance, lawful basis, purpose, collection method, quality limits, retention period, access rights and approved secondary uses. Minimize collection to what the stated objective requires. Define deletion, correction and breach-notification procedures before launch, not after an incident.
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Tell people when AI is used, what role it plays, what data informs it, what the main limitations are and how to reach a human. A notice should be available in the languages and formats users need. Avoid claiming that an explanation is meaningful if it merely repeats technical terms.
Make challenge and human review real
Provide a route to question an output, submit relevant evidence and receive a timely decision from a person with authority to change it. Record the appeal, the original output, the reviewer’s reasoning and the resolution. OECD principles place transparency, explainability, human agency and oversight, privacy and accountability together because one cannot compensate for the absence of the others.
Step 6: Test before release and during use
Use representative validation data
Separate development, validation and holdout data, and document how each reflects the intended population and operating conditions. Where labels are subjective or historically biased, assess label quality and obtain domain review. Do not treat a high average score as evidence that every subgroup is served safely.
Combine technical and social testing
- Subgroup analysis: Compare outcomes, errors, access and burden across relevant groups.
- Red-teaming: Try to trigger unsafe behavior, privacy leakage, manipulation and prohibited use.
- Human-factors review: Observe whether users understand, question and appropriately override outputs.
- Real-world pilot: For high-consequence or difficult-to-reverse uses, test in the actual service context with safeguards and a limited population.
- Independent challenge: Invite affected communities, domain experts and rights specialists to examine assumptions and failure scenarios.
UNESCO calls for systems with potential rights risks to be broadly tested, including in real-world conditions where needed, as part of an ethical impact assessment.
Record unresolved risks and acceptance owners
Maintain a decision log showing each known risk, evidence, mitigation, residual exposure, owner and review date. If a risk is accepted, name the person or body with authority to accept it and specify what new evidence would reopen the decision. Do not hide uncertainty behind a single pass/fail label.
Step 7: Establish governance and accountability
Assign named lifecycle owners
| Responsibility | Minimum duty |
|---|---|
| Data owner | Approve provenance, quality, lawful use, retention and access controls. |
| Model owner | Maintain versions, evaluation results, limitations, update criteria and rollback artifacts. |
| Procurement owner | Set evidence, audit, portability, security, service-level and exit requirements. |
| Operational owner | Ensure trained users, human review, accessibility and continuity procedures. |
| Incident owner | Receive reports, triage severity, preserve evidence and coordinate response. |
| Appeals owner | Provide an accessible challenge route and track resolution quality and timing. |
| Monitoring owner | Review drift, subgroup effects, benefits, harms, labor and environmental indicators. |
| Decommissioning owner | Execute safe shutdown, data deletion, notice and transition to an alternative process. |
Keep versioned documentation and audit trails
Version the model, prompts or rules, datasets, evaluation sets, policies, user guidance and deployment configuration. Log material inputs, outputs, overrides, incidents and appeals while protecting personal data. An audit trail should let an authorized reviewer reconstruct what happened and which version was active.
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Use proportionate, risk-based controls
OECD’s government framework groups controls into enablers, guardrails and engagement and recommends a systems approach with proportionate, risk-based measures. Low-risk experimentation may need lightweight documentation; systems affecting rights, safety or essential services require stronger testing, independent challenge and continuous oversight.
Step 8: Measure long-term impact after launch
Launch is the beginning of impact measurement. Establish a baseline, measurement cadence, responsible owner and threshold for intervention for each indicator. Reassess after a material model, data, policy, supplier or context change.
| Impact domain | What to monitor | Possible trigger for action |
|---|---|---|
| Intended benefit | Outcome improvement against the pre-AI baseline, by subgroup and location. | No material improvement, or improvement concentrated in already advantaged groups. |
| Unintended harm | Complaints, adverse events, exclusion, false decisions and downstream effects. | Severity or rate exceeds the approved threshold. |
| Distribution | Who receives benefits, who bears costs and whether gaps widen. | Persistent or growing disparity without a justified explanation. |
| Privacy and security | Access violations, leakage, attacks, retention and secondary use. | Confirmed breach, repeated control failure or new threat. |
| Human agency | Override rates, appeal access, review quality and time to correction. | Review becomes nominal, appeals are inaccessible or corrections are delayed. |
| Environment | Energy, infrastructure, hardware and disposal effects relevant to the deployment. | Impact rises beyond the approved sustainability boundary. |
| Labor | Task changes, workload, deskilling, job quality and safety. | Unplanned displacement, intensified monitoring or unsafe workload. |
| Institutional capacity | Local skills, maintenance ability, data governance and ability to operate without the supplier. | Critical capability is lost or renewal depends on one vendor. |
| Power and concentration | Portability, switching costs, supplier concentration and control over standards or data. | Exit becomes impractical or one provider controls an essential function. |
Set rollback, stop and sunset criteria
Define in advance who can pause the system, what evidence is sufficient, how affected people will be notified and which manual process will take over. A rollback plan should include a known-good model or procedure, data restoration, credential revocation, communications and a review of decisions made during the affected period. A sunset date or renewal gate prevents an experimental system from becoming permanent by inertia.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare candidate systems
When several systems could address the same objective, compare them on evidence rather than marketing claims. Use a weighted matrix that reflects the severity and reversibility of the use case.
| Comparison axis | Evidence to request |
|---|---|
| Social benefit | Defined objective, baseline, outcome evidence and limits of applicability. |
| Harm profile | Failure modes, severity, reversibility, safeguards and incident history where available. |
| Fairness and accessibility | Subgroup results, language and disability support, non-digital access and burden analysis. |
| Privacy and security | Data flows, lawful-use documentation, retention, access controls, testing and breach response. |
| Transparency and contestability | User notice, explanation quality, appeal route, review authority and audit access. |
| Human oversight | Override, repair, safe-decommissioning and continuity procedures. |
| Local fit | Language, norms, institutional processes, infrastructure and locally relevant data. |
| Total cost and capacity | Integration, staff training, maintenance, connectivity, monitoring and transition costs. |
| Portability and concentration | Open interfaces, data export, model replacement, switching costs and supplier dependence. |
| Monitoring quality | Drift detection, subgroup dashboards, incident channels and review cadence. |
| Exit credibility | Tested rollback, manual fallback, data return or deletion and a funded sunset plan. |
Do not award a system a high social-good score because it is inexpensive or novel if the organization cannot monitor it, challenge its outputs or leave the supplier.
Why local context determines development impact
The World Bank’s World Development Report 2026 describes an “adopt, adapt, and advance” pathway. Adaptation to local languages, institutions, data and needs is an impact condition, not an optional implementation detail. A tool trained in high-income contexts may produce recommendations that conflict with local norms, infrastructure or professional practice.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe report notes that AI may help address skill shortages in health care, agriculture, education and government, but benefits are not guaranteed or equally distributed. Concentration in a small number of firms and countries can create dependency. Public institutions should therefore build the ability to test, evaluate, procure and expand successful solutions rather than treating an imported model as a finished policy.
For a development deployment, include these questions in the assessment:
- Does the system support the languages people actually use, including local terminology and speech patterns?
- Can it operate with intermittent power, limited connectivity and available hardware?
- Are local professionals involved in data definition, validation, deployment and monitoring?
- Who owns or can access the data generated in the country or community?
- What skills and institutions remain after the project ends?
- Can the service continue if funding, a contractor or a network connection changes?
As the World Bank puts it, “Developing countries do not need to build trillion-dollar, all-purpose models to benefit from AI.” A smaller, well-adapted system with accountable governance may create more durable value than a larger model that cannot be supervised locally.
Institutional principles to anchor the assessment
UNESCO identifies four core values: respect for human dignity and human rights; just, peaceful and interconnected societies; diversity and inclusiveness; and a flourishing environment and ecosystems. Its framework sets out 10 principles, including proportionality and do no harm, safety and security, privacy and data protection, multi-stakeholder governance, responsibility and accountability, transparency and explainability, human oversight, sustainability, awareness and literacy, and fairness and non-discrimination. It also identifies 11 policy action areas for implementation.
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OECD’s work provides operational guidance on transparency, risk management, auditing, skills, procurement and stakeholder engagement. More than 200 AI policy instruments had been developed worldwide by 2025, and more than 120 countries backed a 2024 United Nations General Assembly resolution promoting safe, secure and trustworthy AI. Those figures show broad policy activity, but they do not prove that any individual deployment is safe or effective; project-level evidence remains necessary.
A practical approval checklist
Before approving a deployment, require documented answers to every applicable item:
- The legitimate objective, intended beneficiaries, baseline and measurable failure conditions are written down.
- AI necessity and proportionality have been compared with lower-risk alternatives.
- Direct users, affected people, bystanders, workers, vulnerable groups, institutions and environmental effects are mapped.
- Power asymmetries, consent or notice, appeal rights and decision authority are explicit.
- Foreseeable misuse, model failure, distribution shift, data leakage, attack and cascading effects have owners and controls.
- Outcome, error, access and burden measures are selected for relevant groups and operating conditions.
- Privacy provenance, lawful basis, minimization, retention, access and secondary-use limits are documented.
- Representative testing, subgroup analysis, red-teaming, human-factors review and real-world pilots are complete where warranted.
- Unresolved risks have acceptance owners, review dates and evidence thresholds.
- Data, model, procurement, operations, incidents, appeals, monitoring and decommissioning owners are named.
- Versioned documentation and audit trails are usable without exposing unnecessary personal data.
- Monitoring covers benefits, harms, distribution, privacy, security, environment, labor, institutional capacity and concentration of power.
- Rollback, stop, sunset and manual-continuity procedures have been rehearsed.
What a credible verdict looks like
The strongest conclusion is often conditional rather than a simple yes or no: approve a limited deployment for a defined population, with specified safeguards, monitoring thresholds, an appeal channel and a scheduled review. If evidence is insufficient for a high-consequence use, defer deployment and invest in data quality, local capacity or a safer alternative.
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Social benefit is demonstrated over time, not declared at launch. A system earns trust when people can understand its role, challenge its decisions, correct its errors and stop it when conditions change—and when the institution can show that benefits are reaching the intended population without shifting hidden costs onto those with the least power.
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