Work, adoption & judgment · Field note

Build one evidence record for the AI decision and your part in it

TL;DR

A sponsor deciding on an AI program asks what can be inspected. A later employer or promotion panel asks what you personally did. Program documents describe the service, not your choices, and memory favors the person remembering. The paper recommends one record, written while the program runs and checked by others.

What the paper develops

Suppose you lead an AI program and your next funding decision is close. The demonstration works. The sponsor, the executive who approves the money, asks: What can we inspect that shows this works outside the demonstration, for which uses, and who answers for it? Later, another employer, a promotion panel, or a client will ask what you personally did and what came of it.

This paper expects that two beliefs carry a leader through both questions. One is that the program's own documents will answer them. The other is that you will remember what you did. Neither holds up well. The usual documents describe the AI service and how it is managed, not who made each decision, and memory favors the person remembering.

This paper recommends one record, written while the program runs and built around a single claim the decision depends on. The sponsor reads it to decide about the program. Later, you draw a short statement of your own part from it. A personal file of achievements may not be checked by anyone. This record is read and tested by other people while the work is fresh. No study tests whether such a record changes a funding decision or a hiring outcome, so it is this paper's design, not a proven method.

The program's documents describe the service, not your choices

"The Demo-to-Production Gap" covers the written approval for the AI tool itself. The record here is the working file that supplies the evidence behind such an approval, and it adds a trail of who made which choice. Published guidance offers categories for a record about an AI service: purpose, performance, monitoring, incident response, and documented responsibility. Examples are FactSheets, NIST's Generative AI Profile and AI Risk Management Framework, and the GAO's accountability framework. None of it shows that buyers or boards use those categories, and none records which person made which choice.

Memory favors the person remembering

Ross and Sicoly ran five experiments with married couples, students, and basketball players. People recalled more of their own contributions to joint work than of the others', and they claimed more responsibility than the others credited to them. The studies did not cover workplaces or leaders, so the result is a warning, not a measurement. In one experiment, notes did not clearly change who people credited, and the authors suspected the notes were too brief. This paper infers that the record should tie each choice to the decision it shaped and name the other contributors.

What the later reader looks for is only partly known. In LinkedIn's own survey of 1,271 recruiting professionals, 93 percent said that accurately assessing a candidate's skills is crucial for improving the quality of a hire. That shows only that recruiters rate skills assessment as important.

One claim, six fields

You write the record, the sponsor reads it, and you update it at each decision. Pick the claim by asking whether the sponsor would decide differently if it were false. Then fill in six fields. They are the claim, which is about the program; the context and work, including who made each choice and who else contributed; the evidence itself; the result or learning; the limits; and the owner and next decision.

A weak field means evidence exists but covers little, so the claim is narrowed to what the evidence covers. A missing field means no evidence exists yet, so the claim stays out of the decision and out of any later account until it does. A limits field left empty counts as missing. Someone outside the program checks the claim against its evidence, and the people named confirm their entries.

What can go wrong

The record shows the sponsor where your evidence stops and what went wrong, and the paper cannot remove that cost. Confidential work stays private, anonymized, or described through a method. The record belongs to the program and the employer, so check the confidentiality terms with a manager or legal contact before keeping a copy or sharing a statement of your part. A record can be gamed or turn into paperwork, so attach the evidence itself and treat an empty limits field as missing. As planning estimates, not measurements, expect about two hours to set up one record and about fifteen minutes to update it, plus short checks by others. Nothing here shows the record works, and credentials, reputation, and trusted relationships continue to matter.

What to do next

Before the next decision that matters, pick one claim and fill in the six fields. Give the sponsor the record before the meeting. Narrow or hold any claim the evidence cannot support. When you leave the program, write a short statement of your own part from the record while it is still in front of you.

What to do next

Before the next decision that matters, pick one claim and fill in six fields: the claim, the context and work, the evidence, the result or learning, the limits, and the owner and next decision. Give the sponsor the record before the meeting, have someone outside the program check it, and narrow or hold any claim the evidence cannot support.

HANDOFFSLEARNINGRECOVERY

Inside the white paper

  • Why program documents and memory both fall short of what a later reader asks
  • The six fields of the record, who checks it, and what a weak or a missing field means
  • The objections: exposure, confidentiality, paperwork, cost, and what no source shows

Sources and notes

  1. Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu, David Piorkowski, Jason Tsay, and Kush R. Varshney, "FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity," arXiv:1808.07261, submitted August 22, 2018, revised February 7, 2019 — a research team proposed FactSheets, a document the supplier fills in to describe an AI service's purpose, performance, safety, security, and provenance. It is a proposal, not a study of adoption.
  2. National Institute of Standards and Technology, "Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile," NIST AI 600-1, July 2024 — NIST's Generative AI Profile suggests voluntary actions for monitoring, evaluation, and incident response across an AI system's life. It does not prescribe a record.
  3. National Institute of Standards and Technology, "AI Risk Management Framework Core," excerpt from AI RMF 1.0, 2023 — NIST's AI Risk Management Framework Core asks organizations to document roles and responsibilities for managing AI risk and to plan periodic review. It is voluntary and does not cover one person's account of their part.
  4. U.S. Government Accountability Office, "Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities," GAO-21-519SP, June 30, 2021 — the U.S. Government Accountability Office lists governance, data, performance, and monitoring practices for managers, auditors, and third-party assessors of AI systems. It offers categories, not evidence that organizations use them.
  5. Michael Ross and Fiore Sicoly, "Egocentric Biases in Availability and Attribution," Journal of Personality and Social Psychology 37(3), 322-336, 1979 — five experiments with married couples, student groups, basketball players, and graduate students found that people recall more of their own contributions to joint work and claim more responsibility than others credit them. In one experiment, notes did not clearly change who people credited, and asking students about their supervisors first shifted credit toward the supervisors. The studies did not cover workplaces or leaders.
  6. Greg Lewis, "LinkedIn Report: How AI Will Redefine Recruiting in 2025," LinkedIn Talent Blog, February 13, 2025 — LinkedIn's research team surveyed 1,271 recruiting professionals, and 93 percent said accurately assessing a candidate's skills is crucial for improving quality of hire. This is LinkedIn's own research on its members. It records a belief, not that a record changes a hiring decision.