AI can make a task easier while making the surrounding system harder to question. A faster benefits application might help residents; an unexplained rejection might leave them stuck. Both possibilities belong in a serious conversation about AI and society.
The useful question is not whether AI is entirely good or bad. It is what a particular system changes, who benefits, who carries the risk, and whether people can challenge mistakes. That approach makes more sense than treating every new chatbot as evidence of either inevitable prosperity or inevitable collapse.
Start with the decision being changed
“AI” covers different technologies and uses. Software that predicts demand, a model that generates text, and a tool that ranks applications can have very different consequences. Knowing that an organization uses AI tells you surprisingly little without knowing its job.
Consider this illustrative example: a local agency wants to summarize incoming letters. A draft summary that a caseworker checks is one proposal. Automatically deciding which residents deserve help is another. The same language technology could be involved, but the authority, potential harm and necessary oversight differ substantially.
Ask what happens after the output appears. Does a person check the original information? Can the software trigger an action? Who is responsible when its suggestion is wrong?
Jobs change through tasks
A job usually contains several activities: explaining, organizing, checking, negotiating, handling exceptions and accepting responsibility. Automating one activity does not establish that the entire job will disappear.
The ILO and NASK’s May 2025 research assessed potential exposure to generative AI at task level. It emphasized job transformation as the more likely overall outcome than wholesale replacement. Exposure measures technological potential, not an observed layoff count or a promise of job security.
The practical questions for a workplace are therefore specific. Which tasks are changing? Is training provided during paid time? Who checks the work? Are saved hours used to improve service, increase workloads or reduce staffing? Those are organizational choices, and workers should have a voice in them.
Public benefits need public evidence
It is reasonable to explore AI that helps people understand forms, find information or make services easier to use. But a demonstration is only the start of the evidence.
Imagine a council announces that its new assistant answers questions twice as quickly. That sounds promising until you ask whether the answers are correct, whether people successfully complete applications, and whether callers with complex circumstances still reach someone who can help. Speed alone misses the purpose of the service.
A credible evaluation should compare the new approach with an existing alternative, include people likely to encounter difficulties, and report failures as well as averages. This is an editorial standard for judging projects, not a claim that every institution already follows it.
Who gains, and who is left out?
A service can improve on average and still work poorly for particular languages, disabilities, neighborhoods or unusual cases. Access also includes cost, connectivity and the availability of help. Offering a sophisticated app does little for someone who cannot use the only route into a service.
UNESCO’s Recommendation on the Ethics of Artificial Intelligence places human rights, fairness and human oversight at the center of AI policy. Those principles become meaningful when translated into decisions about access, testing and remedies.
For residents and customers, ask for a usable correction process and an alternative contact route. For organizations, ask whether people affected by the system were involved before it became difficult to change.
Trust and resources are part of the cost
NIST’s 2024 generative AI risk profile identifies problems including fabricated information, harmful bias and environmental impacts. A system can be useful and still create costs that its purchase price does not show.
For example, cheap synthetic content can increase the amount of material readers must verify. A project that saves staff time can also require spending on checking, complaints and maintenance. These are reasons to measure the whole service rather than celebrate the software alone.
A better way to follow the debate
When a headline predicts that AI will save or destroy society, look for the evidence connecting the technology to the claimed outcome. Is it reporting an observed result, a controlled experiment, an economic model or speculation? Are uncertainty and competing explanations visible?
Long-term risks deserve careful research, but confident predictions about civilization are not established facts. You can take present harms seriously without accepting every worst-case story, and welcome useful tools without treating every deployment as progress.
For everyday precautions, continue with five AI risks you can reduce. Understanding the larger tradeoffs and making sensible personal choices are complementary parts of AI literacy.
Featured photograph: JIWON KANG via Unsplash. An everyday city scene, used to illustrate the people affected by technology decisions.
Updated September 24, 2026. This AI-assisted article explains the linked sources and includes clearly labeled illustrative examples where used. It is for general information. See our disclaimer.