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Artificial intelligence can help people and organizations work through certain tasks faster, support research and learning, and inform health and public-service applications. It can also reproduce bias, expose data, produce unreliable results, and make consequential decisions harder to challenge. The five pros and five cons below show why AI’s effects depend on the task, the evidence, and the safeguards around its use.
Five potential benefits of artificial intelligence
1. Better performance on some tasks
AI can help with specific work such as drafting, summarizing, classifying information, or analyzing data. The OECD says initial evidence suggests generative AI can improve performance on particular workplace tasks by about 20 to 40 percent, depending on context. That is a task-level finding, not a promise that a worker, company, or economy will become 20 to 40 percent more productive. The OECD says the longer-term, economy-wide effects remain uncertain. OECD’s artificial intelligence overview
2. Support for healthcare applications
AI can be applied to diagnosis and disease prevention, drug and treatment discovery, tailored interventions, and self-monitoring. These are areas of potential use, not evidence that AI replaces clinicians or improves every patient’s outcome. In healthcare, a tool’s value depends on whether it is suitable for its intended use and how its results are checked and acted on. OECD, Artificial Intelligence in Society
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AI may help researchers process information, spot patterns, and explore candidate solutions, which could accelerate scientific progress. That is a prospective benefit: results depend on the field, the quality of the data and methods, and whether researchers can validate what a system produces. The OECD identifies scientific progress as a potential benefit rather than a guaranteed outcome. OECD, November 2024 policy paper
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4. Additional support for teaching and learning
AI tools may assist teaching and learning, for example by helping users work with information or practice a skill. Whether that support helps a particular learner depends on how the tool is used and evaluated; the OECD material does not establish that AI improves results for every student. OECD’s artificial intelligence overview
5. Improved sense-making, forecasting, and public services
AI may help people and institutions make sense of complex information and develop forecasts, and it has potential applications in public services. Those capabilities can inform decisions, but they do not verify the underlying evidence or make an institution’s choices accountable. The OECD identifies better sense-making and forecasting as prospective benefits. OECD, November 2024 policy paper
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Five risks and disadvantages of artificial intelligence
1. Bias and discrimination
Bias can enter through data, computational design, human decisions, and wider systemic conditions. A system can reproduce or amplify existing disadvantage even if no one intended to discriminate; AI may also increase the speed and scale at which harmful bias affects people. Fairness therefore needs to be assessed across the groups affected by a system, not assumed from its technical design. NIST AI Risk Management Framework NIST’s overview of AI bias
2. Privacy and data exposure
Data used to train or operate AI systems can create privacy risks. Before using a tool, consider what information it collects, how that information is used, and whether affected people can control or challenge that use. Privacy is one of the characteristics NIST includes in trustworthy AI; it should be considered alongside the system’s purpose and context. NIST AI Risk Management Framework OECD’s artificial intelligence overview
3. Safety, reliability, and security failures
An AI system may give unreliable results in a particular setting, produce harmful outputs, or be vulnerable to security attacks. These are distinct problems: a system that performs reliably on one task is not automatically safe in another, and good performance alone does not establish that it is secure. NIST’s framework treats validity and reliability, safety, and security and resilience as separate dimensions to assess. NIST AI Risk Management Framework
4. Opaque decisions and weak accountability
When AI contributes to a consequential decision, people may not understand how the result was reached or know how to contest it. NIST’s framework includes accountability, transparency, explainability, and interpretability among the characteristics to consider. Transparency by itself, however, does not establish that a system is accurate, private, secure, or fair. NIST AI Risk Management Framework NIST’s AI RMF launch account
5. Unequal benefits and concentrated power
AI’s benefits and costs may be distributed unevenly among workers, companies, communities, and countries. The OECD identifies inequality and concentration of power as prospective risks. It also reported that, as of 2023, evidence of negative effects on labour demand was limited while AI adoption remained low. That evidence does not support a claim that AI has already caused economy-wide job losses. OECD, November 2024 policy paper
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There is no single reliable statistic that captures AI’s overall benefit or harm. A more useful assessment focuses on the particular system and decision:
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- Task and evidence: What is the system meant to do, and what evidence shows it performs that task in the relevant setting?
- Who benefits and who bears the costs: Are gains and burdens shared fairly among the people, workers, or communities affected?
- Consequences of error: What happens when the system is wrong, and can a person detect and correct the mistake?
- Data and security: What information is collected or used, and how are privacy and security risks addressed?
- Fairness: Does performance or impact differ across affected groups, and are those differences monitored?
- Oversight and accountability: Can people understand, question, and appeal consequential decisions, with a responsible person or institution answerable for them?
NIST’s AI Risk Management Framework describes these characteristics as matters to balance according to a system’s context. Its framework is voluntary, and NIST cautions that transparency alone does not guarantee other trustworthy properties. NIST AI Risk Management Framework
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