Thank you Brad. 🙏

Thank you Nicholas for your support and inspiration.

Thank you dear Anxo. It means a lot coming from you.

Thanks to the team of co-authors: @nckobis.bsky.social, Zoe Rahwan, Raluca Rilla, Bramantyo Supriyatno, Tamer Ajaj Clara N. Bersch, @jfbonnefon.bsky.social .

And of course, a big 'thank you' to the editor @meharpist.bsky.social for recruiting and collaborating with such knowledgeable and supportive reviewers, and guiding us through this whole process.

So, to the reviewers who handled our manuscript: thank you. While you remain anonymous to us and the world, please know that your contribution is deeply felt and sincerely appreciated.

In science, the authors receive the public credit, but the integrity and quality of our work rest heavily on the shoulders of these unsung heroes. They dedicate their time and expertise not for glory, but for the betterment of science itself.

They challenged our assumptions, pushed us to strengthen our data and analysis, replicate our findings in a more realistic domain (tax evasion) and helped us clarify our story. It is no exaggeration to say that their critical input was essential in elevating the work to the level of the journal.

While our research team is enjoying this wonderful moment, we want to shine a light on the people who work diligently behind the scenes: the anonymous peer reviewers.

This paper was immeasurably improved by their rigorous questioning and thoughtful, and constructive feedback.

Thanks to the combined efforts of lead co-authors @nckobis.bsky.social and Zoe Rahwan, Nils Köbis, in addition to @jfbonnefon.bsky.social, Raluca Rilla, Bramantyo Supriyatno, Tamer Ajaj and Clara Bensch. Thank you to all the support from @arc-mpib.bsky.social @mpib-berlin.bsky.social

✅ Develop robust safeguards & oversight: We urgently need better technical guardrails against requests for unethical behaviour and strong regulatory oversight.

✅ Preserve user autonomy: A remarkable 74% of our participants preferred to do these tasks themselves after trying delegation. Ensuring people retain the choice not to delegate is an important design consideration.

🧭 The Path Forward
Our findings point to several crucial steps:
✅ Design for accountability: Interfaces should be designed to reduce moral ambiguity and prevent users from easily offloading responsibility.

🚧 The Guardrail Problem

Built-in LLM safeguards are insufficient to prevent this kind of misuse. We tested various guardrail strategies and found that highly specific prohibitions on cheating inserted at the user-level are the most effective. However, this solution isn't scalable nor practical.