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Ask candidates to move from slogans to decisions: which level of government should act, what exact rule or program they would support, who would enforce it, and what evidence would show that it worked. The questions below cover federalism, elections, transparency, privacy, civil rights, public-sector use, accountability and remedies without assuming that proposals or international frameworks are already U.S. law.
Start with the question every candidate should answer
For any AI proposal, ask: “What would you do first, who would be responsible, and what public evidence would show whether it worked?” A serious answer should identify a legal mechanism, a responsible agency or government, a timetable, a measure of success and a way for the public to challenge failures. If a candidate cannot name those elements, the position is probably a slogan rather than an actionable policy.
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Ask the same follow-up after every answer. It makes candidates comparable even when they use different language about innovation, safety or competitiveness.
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AI policy is partly a federalism question. The United States has federal agencies, Congress, states, local governments and courts that can affect the same system. A candidate should explain not only the desired protection, but also the constitutional or statutory route for delivering it.
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Questions to ask
- Which AI rules would you support at the federal level, and which should remain with states?
- If you want one national standard, what protections must it guarantee, and how should the public hold the federal government accountable?
- Which specific law, executive action or agency policy would you change or support? What is its current legal status?
- How would your approach handle conflicts between a federal standard and stricter state protections?
These questions are especially timely because Executive Order 14365, dated December 11, 2025, directs the executive branch toward a uniform national AI policy framework and actions challenging certain state laws. It also directs work on federal reporting and disclosure standards and a legislative recommendation. The order describes the administration’s direction; it is not evidence that Congress has enacted a comprehensive federal AI law. A candidate should therefore distinguish an executive instruction from a statute, regulation or court ruling.
2. What should election offices be allowed to do with AI?
Election administration raises different issues from campaign advertising. Offices may consider AI for translation, voter-service questions, accessibility, document processing or other administrative work, but an error can affect eligibility, instructions or public confidence. Ask candidates to separate beneficial assistance from decisions that require accountable human judgment.
Questions about election-office use
- What uses of AI by election offices should be allowed, restricted or prohibited?
- Which tasks must always receive human review before information reaches voters or a ballot process is affected?
- How should election offices secure AI-assisted systems, vendor connections and the data used to operate them?
- What is the correction process when an automated tool publishes inaccurate election information?
- Who must notify the public about an error, and how quickly?
The introduced Preparing Election Administrators for AI Act would direct the Election Assistance Commission, in consultation with the National Institute of Standards and Technology, to issue voluntary guidance on benefits and risks, cybersecurity, accurate election information and AI-generated disinformation. The proposal also calls for a study of AI use and impacts in the 2024 federal elections. Its introduced text does not create current obligations, so ask a candidate whether they support the bill, a different measure or no federal role, and check its status before treating it as pending law.
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Generative tools can create realistic audio, video and images. The policy choice is not simply whether to ban them. Rules may need to distinguish materially deceptive impersonation from obvious parody, satire, artistic work and ordinary political commentary. Enforcement also matters: a disclosure requirement with no practical route for complaints may not protect voters.
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Questions about campaign content
- Should campaigns disclose realistic AI-generated or materially altered political media?
- What threshold would trigger a label: any synthetic element, a realistic depiction of a real person, or content likely to mislead about a candidate or election?
- Who would enforce the rule—an election agency, another regulator, courts or platforms?
- What exceptions would protect satire, parody, journalism and ordinary political speech?
- How would the rule apply close to Election Day, when a correction may arrive too late to reach the same audience?
Ask candidates to describe the notice itself, where it would appear, how long it would remain attached to copied material and what penalties or remedies would follow a violation. Those details reveal whether a proposal is workable rather than merely protective in principle.
4. When should people be told they are seeing or talking to AI?
Transparency can mean several different things: notice that a person is interacting with a machine, disclosure that content was generated or altered, documentation for regulators, or an explanation of how a decision was reached. Candidates should say which form they mean and in which settings it would apply.
Questions about notice and disclosure
- When should a person be told that they are interacting with an AI system?
- When should viewers be told that an image, video, audio clip or text was AI-generated or materially altered?
- Should disclosures be required for every use, or only where realism, scale or potential harm creates a significant risk?
- What information should a public agency provide about a system’s purpose, limits, data and operator?
- How should a person challenge a missing, misleading or inaccessible disclosure?
The Council of Europe’s convention on artificial intelligence and human rights, democracy and the rule of law treats transparency and oversight as context- and risk-sensitive and includes identification of AI-generated content. It is an international comparative framework, not U.S. law. A candidate may use it as a model, but should identify the domestic authority that would implement any similar requirement.
5. What data and civil-rights safeguards are required?
AI systems can collect, infer or reuse personal information. They can also produce unequal error rates or discriminatory effects, including when a seemingly neutral model is trained on skewed data or used in a high-impact setting. Ask for protections before deployment, testing after deployment and a response when testing finds a problem.
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Questions about privacy and discrimination
- What personal data should AI systems be allowed to collect, retain or use, and for what purposes?
- Should people have access, correction, deletion or opt-out rights for data used by a system?
- How would you test for discriminatory outcomes across relevant groups and real-world conditions?
- Who must publish those test methods and results?
- What must happen when testing identifies unequal accuracy or harmful treatment?
- How would your proposal protect privacy and civil rights while allowing legitimate research and public services?
The Council of Europe convention identifies privacy and personal-data protection, equality and non-discrimination, reliability, accountability and risk management as governance concerns. It says safeguards should fit the context and risk. That principle lets voters ask why a low-risk administrative tool would receive different controls from a system influencing employment, housing, benefits, policing or access to health care.
6. Which high-impact decisions need a human in the loop?
“Human oversight” is meaningful only when the human has authority, time, information and training to reject an automated recommendation. A person who can merely click approve is not an effective safeguard. Ask candidates to identify the decisions that require review and the conditions under which an automated result must be stopped.
Questions about oversight
- Which AI uses in public services or decisions affecting rights require human review before action?
- Can the reviewer overturn the system, and must the agency record that decision?
- What documentation must exist about the model, data, known limitations, incidents and updates?
- Who monitors risks after deployment rather than only during procurement?
- What independent audit or inspection powers would regulators, legislators or the public have?
Ask how oversight would work across the system’s lifecycle: design, procurement, testing, deployment, updates, incident response and retirement. A policy limited to pre-release approval can miss failures that appear only when conditions, users or data change.
7. Who is responsible when an AI system harms someone?
Responsibility can be divided among a developer, vendor, agency, employer, campaign, operator or official. A candidate should not answer “the algorithm.” Ask who has a legal duty to prevent harm, preserve records, explain a decision and provide a remedy.
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Questions about accountability and remedies
- If an AI-assisted decision harms someone, what explanation, appeal, correction or remedy should be available?
- Who is liable when a government agency relies on a vendor’s system?
- Must affected people be told that AI contributed to a decision about them?
- What records must be preserved so an independent reviewer can reconstruct what happened?
- Which regulator or court would hear a complaint, and how would people without money or technical expertise use that process?
The Council of Europe convention addresses accountability for adverse impacts and accessible remedies in the circumstances covered by its provisions. It also discusses reliability, risk assessment, mitigation, monitoring and documentation. Those are useful comparison dimensions, but the convention itself does not create a U.S. cause of action or guarantee a remedy under American law.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. How should government adopt AI responsibly?
Government adoption involves more than buying software. Agencies must define the problem, compare an AI system with non-AI alternatives, protect sensitive information, manage vendors and measure whether the service actually improves. Ask candidates what evidence must exist before a pilot becomes a permanent program.
Questions about public-sector adoption
- Where should government adopt AI to improve services, and where should it not use it?
- What evidence of accuracy, accessibility, security and cost would be required before scaling a system?
- What privacy, civil-rights, cybersecurity and procurement safeguards would apply to contractors?
- How would agencies avoid dependence on a vendor that controls the model, data format or ability to export records?
- Who can suspend a system when incidents or unacceptable error rates appear?
A White House announcement dated April 7, 2025 described revised federal AI-use and procurement policies as encouraging adoption while maintaining protections for privacy, civil rights and civil liberties. That announcement states the administration’s approach; it is not independent evidence that safeguards work in every deployment. Ask candidates for measurable requirements, such as published impact assessments, incident reporting, appeal timelines or error thresholds, rather than accepting policy labels alone.
9. What can voters learn from international rules?
International frameworks can clarify choices, but geography and legal effect must stay explicit. The European Commission’s guidance on Article 50 of the EU AI Act describes transparency obligations for specified providers and deployers, applying from August 2, 2026 within the European Union’s legal framework. It does not govern U.S. candidates, voters or agencies merely because it exists.
Ask a candidate whether an international example is being offered as a model, a trade requirement, a voluntary best practice or a rule they would actually enact domestically. Then ask which U.S. institution would have authority to implement it and how conflicts with existing law would be resolved.
10. Compare answers using the same scorecard
After a debate, town hall or written response, record each candidate’s answer against the same dimensions. This prevents a confident general statement about innovation from outranking a detailed plan from another candidate.
| Comparison dimension | What to look for |
|---|---|
| Level of government and legal mechanism | Federal statute, executive action, agency rule, state law, local policy or voluntary standard; current legal status and limits. |
| Scope | Which systems, sectors, data and uses are covered, and which are exempt. |
| Transparency and notice | When people receive a disclosure, what it says, how it remains attached to copied content and how complaints work. |
| Privacy and civil rights | Data limits, retention, access, testing for unequal outcomes and required correction. |
| Human oversight and appeal | Who can override a system, what records are kept and how an affected person challenges a result. |
| Election security and information integrity | Permitted election-office uses, cybersecurity, correction procedures and campaign-media disclosures. |
| Accountability and enforcement | Named responsible parties, regulator powers, penalties, audits and accessible remedies. |
| Evidence of benefits and harms | Baseline comparisons, pilots, independent evaluation, public reporting and a trigger for stopping or changing the program. |
How to spot a weak answer
- It names a principle but no instrument: “We support safe AI” without a bill, rule, budget or agency action.
- It treats a proposal as law: an introduced bill or executive order is described as if it already imposes obligations.
- It ignores jurisdiction: an EU requirement or international convention is presented as binding in the United States.
- It promises transparency without a remedy: people are told about a system but cannot correct, appeal or challenge its decision.
- It claims human oversight without authority: a reviewer is responsible on paper but cannot see the evidence or stop the system.
- It cites innovation without measurement: no baseline, timeline, incident report or public test would show whether adoption helped.
A compact question set for a town hall
- Which AI rules belong at the federal level, which belong to states, and what legal mechanism would you use?
- What may election offices do with AI, how must those systems be secured, and how would inaccurate information be corrected?
- Should realistic AI-generated or altered campaign media be labeled? Who enforces that rule, and how are satire and parody protected?
- When must people be told they are interacting with AI or viewing synthetic content?
- What data, privacy and civil-rights safeguards must apply, and how will discriminatory outcomes be tested and corrected?
- Which high-impact decisions require meaningful human review and an appeal?
- Who is responsible when an AI-assisted decision causes harm, and what remedy can the affected person obtain?
- What evidence must a public agency show before adopting or expanding an AI system?
Demand a concrete first step, a named decision-maker and public evidence of results after each answer. That standard lets voters compare candidates on choices and accountability rather than on optimism or fear.
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