This is a linkpost for the position paper AI Welfare Is Bullshit by Yunze Xiao, Gordon Dai, Shahan Ali Memon, Jen-tse Huang, Maarten Sap, and Mona Diab, whose preprint was published on 14 April 2026. The abstract is below. Here is summary of the paper from Yunze. "Comments, pushback, and counter-cases are welcome — especially from researchers actively building welfare benchmarks. The argument is meant to provoke a methodological standard, not to shut down inquiry".
Recent proposals urge AI labs to prepare for “AI welfare” under uncertainty about whether AI systems have morally relevant inner states. We do not argue for or against the possibility of AI welfare. Instead, we argue that current AI welfare assessment fails for two linked structural reasons absent from other evaluation targets. First, AI welfare indicators are co-engineered with the systems they evaluate: ordinary development decisions that shape model behavior can also manufacture or suppress welfare evidence. Second, AI welfare lacks external validation: no deployment failure or independent test can reveal whether a welfare metric tracks anything real about the system. Together, these problems yield our central claim: For current systems, AI welfare is bullshit in Frankfurt’s sense, as its measurement regime is structurally disconnected from truthtracking [see On Bullshit]. AI welfare should therefore not be institutionalized as a binding gate for oversight, release, or accountability; restrictions on AI systems should instead be justified by externally verifiable harms.