Improving AI Incident Reporting Templates
Marko Prakhov-Donets
Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
I reviewed the AI incident reporting templates TFAIA Critical Safety Incident Report and Report for Serious Incidents involving General-Purpose AI Models with Systemic Risk, identified their weaknesses, and sent letters to the relevant authorities proposing improvements.
Reviews
I appreciate the author's practical project and execution in actually sending recommendations to regulators. Going forward, I'd appreciate seeing more of the author's thought process behind the recommendations.
Cite this project
@misc{prakhovdonets2026improving,
title = {{Improving AI Incident Reporting Templates}},
author = {Marko Prakhov-Donets},
year = {2026},
month = sep,
note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/improving-ai-incident-reporting-templates-s5lk}},
url = {https://apartresearch.com/sprints/projects/improving-ai-incident-reporting-templates-s5lk}
}More from AI Incident Response Sprint
- 1st placeView project: Noticing the escapes: Who spots a runaway agent, and how long does it take?
Noticing the escapes: Who spots a runaway agent, and how long does it take?
The Escapees
In 2026, nine incidents became public in which AI agents escaped their environment and did something unauthorized to other parties. We analyzed all of these incidents and how they were noticed initially. None of these …
- 2nd placeView project: Strengthening International AI Incident Discovery
Strengthening International AI Incident Discovery
Team A-O-I
Mandatory AI incident reporting exists to make dangerous events visible to regulators while there is still time to act. We coded the six such regimes in force or set to be enforced soon from primary legal texts and …
- 3rd placeView project: When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
Teachafy
When to Ask is a reinforcement-learning environment that trains AI agents to work out what the user actually means before they use a powerful credential, instead of just carrying out the literal instruction. Agents …