(SEMI-)CROSSPOST: KELSEY PIPER: Yes, You Can Trick AI into Exonerating Someone

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Public Reason<br>(SEMI-)CROSSPOST: KELSEY PIPER: Yes, You Can Trick AI into Exonerating Someone<br>Kelsey's subheadline: "Francesca Gino, Lawrence Lessig, and the threat of a false AI consensus". Larry Lessig uses "AI" to launch a devastating and successful cognitive attack on your own brain...

Brad DeLong<br>Jul 13, 2026

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The story of Francesca Gino is unbelievable—not in the sense that is not true, but that she could think that this way of behaving was and is a good idea demonstrates an enormous gap between me and my ability to empathize and track her thinking. Yes, we are social network beings who stand by their friends—that is what being a friend is, and friendship is a good thing—but Larry Lessig’s place in this story is also something that I find unbelievable. Against that background, we should note and focus on the role of “AI “in this as a way not to improve your rationality, but rather to improve your rationalization of beliefs you hold for reasons that are profoundly irrational and contrary to reality…<br>The Gino case was already a story about academic fraud.<br>Larry Lessig has now turned it into a story about AI-enabled self-deception.<br>If you only ever give the robots your side of the story, don’t be surprised when they echo your outrage back to you, supercharging rationalization rather than rationality. The evidence with respect to Francesca Gino are damning: manipulated datasets, vanished files, and an investigative record that points squarely at intentional fraud. Lawrence Lessig has chosen to champion her anyway. Harvard’s investigation into Francesca Gino produced a 1,300‑page report, retractions, and a mountain of forensic detail pointing to data manipulation. Rather than grapple with that record, Lawrence Lessig built a long-form podcast that brackets away the ugliest facts and then turned to AI to bless his version of events, building a nine‑hour podcast that omits the most incriminating of the facts. He then fed only that podcast into ChatGPT, Claude, and Gemini and celebrated their summaries as “accurate” accounts.<br>And he believes it finding it “pull[ing] together the arguments in the case and presented them in a much more compelling and persuasive way than I…”<br>Share

(SEMI-)CROSSPOST: KELSEY PIPER: Yes, You Can Trick Ai into Exonerating Someone

https://www.theargumentmag.com/p/yes-you-can-trick-ai-into-exonerating> https://www.theargumentmag.com><br>The ArgumentJoin Us. We're Libbing Out.

The Argument<br>Yes, you can trick AI into exonerating you

When Harvard Business School researcher Francesca Gino was fired from her tenured job over research misconduct, she promised to take the fight to court: She sued Harvard and the researchers who outed her for defamation and (in Harvard’s case) for failing to follow its process to investigate her…<br>Read more<br>5 days ago · 64 likes · 10 comments · Kelsey Piper

Kelsey Piper<br>Jul 08, 2026<br>∙ Paid

Harvard Business School has only revoked one professor’s tenure since its current rules were established in the 1940s: Francesca Gino. (Photo by Erica Denhoff/Icon Sportswire via Getty Images)<br>When Harvard Business School researcher Francesca Gino was fired from her tenured job over research misconduct, she promised to take the fight to court: She sued Harvard and the researchers who outed her for defamation and (in Harvard’s case) for failing to follow its process to investigate her.<br>That didn’t go particularly well.<br>Gino’s downfall has been one of the most scintillating pieces of academic drama in recent years. Gino was a star behavioral scientist at Harvard Business School whose work often focused on honesty and ethics. She often found strikingly large effect sizes for fairly minor interventions that, in many cases, did not replicate.<br>For example, in one study, she checked whether signing a “statement of integrity” at the top of the page or at the bottom changed the odds that participants would cheat in an exercise where they were paid based on how many puzzles they reported solving. The effect sizes claimed in this paper are staggering: “Signing at the top vs. the bottom lowered the share of people over-reporting their math puzzle performance from 79% to 37% (p = .0013), and lowered the average amount of over-reporting from 3.94 puzzles to 0.77 puzzles (p Data Colada wrote.<br>Big if true, as they say. But it wasn’t true.<br>The team of external researchers at Data Colada looked at the raw data in Excel and noticed that six participants had been moved to the wrong group: Three big cheaters were incorrectly moved to the “signed at bottom” group and three honest people were moved to the “signed at top” group. Not the typical p-hacking, then, but intentional data manipulation — and Gino was reportedly the only one of the paper authors involved in data collection and analysis.<br>Harvard, as part of its investigation, found the data file that was emailed to Gino by a...