AI can harm society when it spreads convincing false content, carries bias into consequential decisions, intrudes on privacy, disrupts work, concentrates power, or makes important decisions difficult to understand and challenge. These are documented areas of concern—not proof that every AI system causes harm or that its overall effect is uniformly negative.
The effects depend on the system’s design, data and objectives, where it is used, and how it is governed. The most useful way to understand AI’s negative impact is to look at the pathways by which particular systems can create or amplify harm.
What does “negative impact” mean in the available evidence?
Policy reports identify risks and areas requiring attention; they do not establish one global measure of how much harm AI has caused. For example, an OECD paper published on 14 November 2024 organizes its discussion around “ten priority risks.” That is the report’s taxonomy, not a count of confirmed incidents or people harmed. The paper covers concerns including manipulation, fraud, privacy infringement, inequality, concentration of power, cyber activity, and incidents in critical systems. Read the OECD’s 2024 assessment of potential AI risks and policy imperatives.
The European Commission Joint Research Centre’s report, released on 10 June 2025, focuses on generative AI and the European Union. It identifies potential challenges, not inevitable outcomes for every generative system or region. A broader OECD report from 2019 provides foundational policy context on issues such as bias, privacy, inequality, labor-market change, market concentration, and the digital divide; it should not be treated as a current measurement of those effects. See the JRC’s generative AI outlook report and the OECD’s Artificial Intelligence in Society.
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How can AI worsen misinformation and manipulation?
Generative AI can produce human-like text and other content, which may make it easier to create material that misleads people or amplifies misinformation. The JRC identifies misinformation as a potential challenge of generative AI. The OECD’s 2024 assessment also highlights manipulation and disinformation, as well as fraud and democratic harms. These sources describe risks; they do not establish that every generated item is false or quantify how much AI has increased misinformation overall.
The concern is not limited to whether a piece of content is accurate. When misleading material is produced or circulated at scale, it can complicate people’s efforts to judge what is credible and can be used to influence decisions. The likelihood and severity of harm depend on how a system is used and the setting in which its output reaches people.
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How can AI reinforce bias and unfair decisions?
AI systems can reflect patterns in the data and processes used to build or operate them. If those patterns encode existing social inequalities, applying them through automated decisions can make their consequences more consequential for the people affected. The OECD’s policy work identifies the risk that AI can reinforce existing bias, and the JRC includes bias among the potential challenges associated with generative AI.
This does not mean every system is biased in the same way, or that a model’s output alone determines an outcome. The relevant question is how the system performs for the people affected and how its output is used. When a decision has significant consequences, limited transparency or an inability to challenge the result can compound the unfairness.
How can AI threaten privacy?
AI can raise privacy concerns when systems depend on extensive personal data or enable surveillance. The OECD identifies privacy infringement as a priority risk in its 2024 assessment and discusses privacy as a wider policy concern in its 2019 report. The JRC also lists privacy concerns among the potential challenges of generative AI.
The risk depends on what data a system collects or uses, what it can infer, and how those data and inferences are handled. The reports identify the concern but do not establish that all AI systems collect personal information or that every use constitutes a privacy violation.
How does AI affect jobs and economic inequality?
AI can automate or reshape tasks, creating disruption for workers as work changes. The JRC identifies labor disruption as a potential challenge of generative AI; the OECD’s broader policy work also addresses labor-market change and inequality. This supports concern about transitions, not a claim that all jobs will disappear or that the overall employment effect is known.
The distribution of benefits and costs also matters. The OECD has identified inequality and poverty, market concentration, and the digital divide as policy concerns. If gains accrue unevenly or access to relevant technology is unequal, AI may contribute to unequal outcomes. The 2019 OECD report frames these as issues for policy attention, not as a present-day global estimate of how much inequality AI has caused.
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Why do concentration, safety, and accountability matter?
When control or benefits are concentrated, the gains and influence associated with AI may not be widely shared. Market concentration is among the concerns discussed in the OECD’s 2019 policy report, while the OECD’s 2024 assessment identifies concentration of power as a priority risk.
AI use in critical systems can also create safety concerns if failures or unsafe operation have serious consequences. The 2024 OECD paper includes incidents in critical systems among its priority risks and also discusses sophisticated cyber activity. The existence of these risks does not establish that an AI system will fail; it highlights the importance of the system’s role and the consequences of an error.
Finally, when people cannot understand why a consequential decision was made, identify who is responsible, or contest the result, accountability becomes harder. The OECD’s 2024 assessment names accountability gaps as a concern. Its policy discussion points to liability, safety, and risk management as areas requiring attention.
How can you assess the risk of a particular AI use?
Risk is specific to the use, not just the technology label. When evaluating an AI system or policy, ask:
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- Are the data suitable for the people and circumstances the system will affect?
- What personal information is collected or inferred, and how much is necessary for the task?
- Can an affected person understand the decision, appeal it, or obtain human review?
- Is performance examined across the groups affected, and are there clear arrangements for monitoring, liability, and risk management?
These questions draw on risk themes in the OECD and JRC reports; they are a practical synthesis, not a scoring framework published by either organization. EU-specific findings in the JRC report should be read in that policy context rather than assumed to describe every region.
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