Being honest about using AI at work makes people trust you less, research finds
Source: University of Arizona, on Schilke & Reimann, The transparency dilemma (OBHDP 2025) · 2025-05-29
Whether you're using AI to write cover letters, grade papers or draft ad campaigns, you might want to think twice about telling others. That simple act of disclosure can make people trust you less, our new peer-reviewed article found.
As researchers who study trust, we see this as a paradox. After all, being honest and transparent usually makes people trust you more. But across 13 experiments involving more than 5,000 participants, we found a consistent pattern: Revealing that you relied on AI undermines how trustworthy you seem.
Participants in our study included students, legal analysts, hiring managers and investors, among others. Interestingly, we found that even evaluators who were tech-savvy were less trusting of people who said they used AI. While having a positive view of technology reduced the effect slightly, it didn't erase it.
Why would being open and transparent about using AI make people trust you less? One reason is that people still expect human effort in writing, thinking and innovating. When AI steps into that role and you highlight it, your work looks less legitimate.
But there's a caveat: If you're using AI on the job, the cover-up may be worse than the crime. We found that quietly using AI can trigger the steepest decline in trust if others uncover it later. So being upfront may ultimately be a better policy.
Yet our research suggests doing so may backfire. This creates a dilemma for those who value honesty but also need to rely on trust to maintain strong relationships with clients and colleagues. In fields where credibility is essential – such as finance, health care and higher education – even a small loss of trust can damage a career or brand.
The consequences go beyond individual reputations. Trust is often called the social "glue" that holds society together. It drives collaboration, boosts morale and keeps customers loyal. When that trust is shaken, entire organizations can feel the effects through lower productivity, reduced motivation and weakened team cohesion.
If disclosing AI use sparks suspicion, users face a difficult choice: embrace transparency and risk a backlash, or stay silent and risk being exposed later – an outcome our findings suggest erodes trust even more.
That's why understanding the AI transparency dilemma is so important. Whether you're a manager rolling out new technology or an artist deciding whether to credit AI in your portfolio, the stakes are rising.
It's unclear whether this transparency penalty will fade over time. As AI becomes more widespread – and potentially more reliable – disclosing its use may eventually seem less suspect.
There's also no consensus on how organizations should handle AI disclosure. One option is to make transparency completely voluntary, which leaves the decision to disclose to the individual. Another is a mandatory disclosure policy across the board. Our research suggests that the threat of being exposed by a third party can motivate compliance if the policy is stringently enforced through tools such as AI detectors.
A third approach is cultural: building a workplace where AI use is seen as normal, accepted and legitimate. We think this kind of environment could soften the trust penalty and support both transparency and credibility.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
Can employers detect AI-assisted applications and should they be permitted? How do confidence signals and expertise language mislead users' trust calibration?- Does trust loss from AI exposure recover over time in workplaces?
- Do people fear AI more when they use it directly and see its failures?
- Is expertise signaling linked to trust in AI-generated content?
- How often does hidden AI use actually get discovered in practice?
- Does the trust penalty from AI disclosure fade with repeated exposure?
- Who is most affected by the transparency penalty when AI is disclosed?
- Why does disclosure of AI involvement sometimes raise trust instead of lowering it?
- What workplace cultures make professionals more willing to disclose AI use openly?
- Does disclosing AI use in professional services damage client trust and credibility?
- Why does AI disclosure harm trust more when platforms reveal it first?
- Do disclosure policies need to account for trust penalties independent of audience preferences?
- Does labeling AI content trigger skepticism even when audiences say they want honesty?
- Should AI disclosure be framed as trust rather than compliance?
- How does disclosure of AI involvement change across private versus public writing contexts?
- Do readers actually want more AI disclosure than writers provide?
- Why do writers resist disclosing AI use more than readers demand it?
- Do human readers still recognize authors after heavy AI rewriting?
- Does AI assistance distort how readers perceive writer identity and demographics?
- Can disclosure of AI involvement change how evaluators score writing quality?
- How does hiding AI use from readers differ from showing it to collaborators?
- Does writer credibility suffer when readers suspect AI involvement?