Risk from fitness-seeking AIs: mechanisms and mitigations

·Alignment Forum··

Current AIs routinely take unintended actions to score well on tasks: hardcoding test cases, training on the test set, downplaying issues, etc. This misalignment is still somewhat incoherent, but it increasingly resembles what I call "fitness-seeking"—a family of misaligned motivations centered on performing well in training and evaluations (e.g., reward-seeking). Fitness-seeking warrants substantial concern.In this piece, I lay out what I take to be the central mechanisms by which fitness-seeki...

Read full article →

Related Articles

Loss of Oversight: How AI Systems May Become Harder to Audit, Monitor, and Investigate
Jordan Taylor · LessWrong · 38m ago
The Case for Evaluating Model Behaviors
jsteinhardt · Alignment Forum · 20h ago
Mechanistic estimation for expectations of random products
Jacob Hilton · ARC · 5d ago
Multipolar Civilisation Depends on Maintaining an Attacker’s Dilemma
Naci Cankaya · LessWrong · 14d ago
Using Base-LCM to Monitor LLMs
Éloïse Benito-Rodriguez · LessWrong · 14d ago