EmergingInformation integrity, authenticity and democratic resilience

AI Information Integrity Researcher

Also called AI Disinformation Researcher

An AI Information Integrity Researcher studies how misleading AI-generated content is made and spreads, then tests ways to catch or slow it. It matters because trust in your product and your outputs depends on it.

What they do

They study how synthetic text, images, audio and video are produced and how they travel across platforms and communities. They build datasets of real cases, run red teaming exercises against a model or product to see how it could be used to mislead, and test provenance validators and verification tools against those cases. They examine media with forensic methods, trace where content came from, and compare detection approaches. They then design interventions, such as labelling, friction or provenance signals, and evaluate whether those interventions work. They write up findings for engineers, policy colleagues and sometimes the public.

What a strong hire delivers

In the first months, a strong hire delivers a clear map of how misleading AI-enabled content is most likely to arise around your product. They produce a first empirical study with a method others can repeat. They test at least one detection or provenance approach and report honestly where it fails. They give product and policy teams findings they can act on, written in plain language.

How you can tell they are good

Ask them to walk through a past study from question to result. Look for clear method, honest limits and a measured view of what the evidence supports. Ask how they would judge a detection tool. Good candidates talk about false positives, base rates and adversaries who adapt. Be wary of anyone who claims certainty about whether content is true or who relies on one tool's output.

Why it is hard to fill

Almost nobody has this exact title. The work often sits inside trust and safety, research, policy or journalism roles, so the people doing it look different on paper. The mix is rare: forensic skill, social research method, and enough technical depth to red team a model. Many candidates have two of the three. Expect to assess skills over job titles, and be clear about which gap you can train.

Where to find them

Look in digital forensics teams, open source investigation groups and newsroom verification desks. Trust and safety analysts who have worked on coordinated manipulation are a strong source. So are social researchers who study online communities, and academic or nonprofit labs focused on disinformation. Technology companies, model providers, platforms and research institutes employ people doing this work, often under a broader title.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Information Integrity Researcher

  1. Describe a study you ran on misleading content. What was the question, the method and the weakest part of your evidence?

    A strong answer states a clear question, a sampling approach and honest limits. They name the weakest point without prompting. Worry if they only describe findings, overstate certainty, or cannot explain how someone else could repeat the work.

  2. How would you evaluate a tool that claims to detect AI-generated images?

    They discuss test data that reflects real use, false positives and negatives, base rates, and how adversaries adapt. They would test on edited and compressed content. Worry if they trust vendor claims or report a single accuracy figure.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Information Integrity Researcher to study how AI-enabled content can mislead, and to test what works against it. You will work with engineering, policy and product teams. Your findings will shape how we build and release, and how we protect the people who rely on our systems.

What you will do

  • Study how misleading AI-generated text, image, audio and video is made and spreads
  • Run red teaming exercises on our products for misleading content risks
  • Evaluate provenance validators, verification tools and media forensics methods
  • Design and test interventions, and report what works and what does not
  • Build and maintain datasets of real cases with clear documentation
  • Write findings for technical and non-technical audiences
  • Advise teams on claims we can and cannot support

What you will bring

  • Demonstrated empirical work on misinformation, manipulation or content authenticity
  • Practical experience with verification, forensics or provenance methods
  • Sound research design and honest reporting of limits
  • Experience in red teaming or adversarial testing of systems
  • Clear technical writing and communication
  • Judgement about uncertainty, avoiding unjustified claims of truth or falsity

Nice to have

  • Background in trust and safety, journalism or digital forensics
  • Experience with content provenance standards
  • Published or public research on information risks

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Information integrityAdvancedAssess manipulated content and evidential authenticity
  • Content provenanceAdvancedMaintain traceable origin and transformations of media
  • AI red teamingWorkingTest systems adversarially under authorisation
  • Technical communicationWorkingExplain evidence and limitations to relevant readers

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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