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Five potential benefits of artificial intelligence
1. Higher performance on some tasks
AI tools can help people draft, summarize, analyze, or otherwise complete particular tasks. Initial evidence cited by the OECD suggests recent generative AI tools can improve performance on specific workplace tasks by about 20 to 40 percent, depending on context. That is a task-level finding, not a forecast that every worker or the economy as a whole will become 20 to 40 percent more productive; the OECD says long-term, economy-wide effects remain uncertain. OECD: Artificial intelligence
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2. Support for health work
Potential health applications include helping with diagnosis and disease prevention, identifying candidates for drugs or treatments, tailoring interventions, and supporting self-monitoring. These are areas of application, not evidence that AI replaces clinicians or improves every patient outcome. Any use in care needs to be evaluated for the specific task and patient population. OECD: Artificial Intelligence in Society
3. Tools for scientific discovery
AI may help researchers analyze large bodies of information and explore possible solutions, potentially accelerating scientific progress. Whether it does so in a particular field depends on the quality of the system and the research process; a prospective benefit is not proof of a demonstrated result. OECD: Artificial intelligence and the changing demand for skills in the labour market
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4. More learning and teaching support
AI may support teaching and learning, for example by assisting with learning materials or activities. Its usefulness depends on how a tool is used and evaluated. The OECD identifies education as a potential benefit area; that does not establish improved results for every learner or setting. OECD: Artificial intelligence
5. Better sense-making, forecasting, and public services
AI can help people and institutions process complex information, identify patterns, and explore forecasts. The OECD identifies better sense-making and forecasting as prospective benefits and discusses potential roles in public services. These tools can inform decisions, but they do not replace checking the evidence or assigning responsibility for a decision. OECD: Artificial intelligence and the changing demand for skills in the labour market
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Five risks and disadvantages of artificial intelligence
1. Bias and discrimination
Bias can arise from data, technical design choices, and wider human or systemic factors. A system can reproduce or amplify existing disadvantage even when no one intended it to discriminate. NIST warns that AI can increase the speed and scale of harmful bias, so evaluations need to consider how outcomes differ for affected groups. NIST: AI Bias
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2. Privacy and data exposure
Data used to train or operate an AI system can create privacy risks. Before using a system, consider what information it collects, how that information is used, and whether people can control or challenge that use. Privacy is one of the trustworthy-system concerns identified by both NIST and the OECD. NIST: AI Risk Management Framework · OECD: Artificial intelligence
3. Safety, reliability, and security failures
An AI system may be unreliable for a particular task, generate harmful outputs, or be vulnerable to security attacks. These are related but distinct concerns: a system can be accurate in one setting yet unsafe or insecure in another. NIST’s framework treats validity and reliability, safety, and security and resilience as separate characteristics to assess in context. NIST: AI Risk Management Framework
4. Opaque decisions and weak accountability
People affected by an AI-assisted decision may not be able to understand how it was reached or how to challenge it. NIST’s framework includes accountability, transparency, explainability, and interpretability, but transparency alone does not guarantee accuracy, privacy, security, or fairness. An explanation is not a substitute for a way to review and correct harmful outcomes. NIST: AI Risk Management Framework
5. Unequal gains and concentrated power
AI’s benefits and costs may be distributed unevenly among workers, firms, communities, and countries; the OECD identifies inequality and concentration of power as prospective risks. This concern should not be turned into a claim that AI has already caused economy-wide job losses: an OECD paper reported little evidence of negative labour-demand impacts as of 2023, while also noting that adoption remained low. OECD: Artificial intelligence and the changing demand for skills in the labour market
How to judge a particular AI use
“AI” covers many kinds of systems and applications, so a broad claim about its overall benefit or harm is not a useful substitute for evaluating a specific use. The sources cited here do not establish a single reliable statistic that captures AI’s total benefit or harm. For a concrete tool or decision, ask:
Best Value
- What task is it doing? Look for evidence on that task, rather than assuming results transfer to other settings.
- Who benefits, and who bears the costs? Consider workers, customers, communities, and groups affected by errors.
- What happens if it is wrong? The consequences should shape how much human review and caution are needed.
- What data does it use? Check what is collected, how it is handled, and whether people have meaningful control.
- Are outcomes fair across affected groups? Compare results and look for harms that may be amplified.
- Can a person understand, review, and challenge the decision? Identify who is accountable and how errors can be corrected.
NIST’s AI Risk Management Framework describes trustworthy characteristics that should be balanced for the system’s context; no single characteristic, such as transparency, settles whether a system is trustworthy. NIST describes the framework as voluntary and has indicated that version 1.0 is being revised, so it should not be treated as necessarily the latest edition. NIST: AI Risk Management Framework
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