Language models are increasingly used to make decisions about people, such as whom to interview, which insurance claims to approve and which patients to see first. Research on bias in these decisions has focused on whether a person's race or gender changes the outcome. However, developers also write text that the model reads before every decision, including a persona that gives the model a role and often names a project, team or office. Whether such text affects whom a model selects remains largely unexamined. We asked what happens when a string in the persona also appears in one candidate's record. Across four kinds of decision, fourteen publicly released models and 328,480 choices between two candidates, instruction-tuned models, the kind used in chat assistants, selected the candidate whose record repeated the persona's string in 71.3% of decisions between equally qualified candidates, compared with 50.1% when no string was shared. When that candidate was one step less qualified, the rates were 31.5% and 11.7%. A made-up project code was enough. The effect persisted when the record labelled the string a conflict of interest, even in models that otherwise selected such a candidate less often. When the shared string was the candidate's race, a similar shift appeared. Bias audits built around protected attributes can at most show the resulting gaps between groups, not their source.
Artificial intelligence (AI) agents built on large language models increasingly work in teams, where one agent divides tasks or resources among others that the software marks with a team, a role or a similar label. A model that simply up-weights any recipient sharing a string with its own context would favor same-label peers, so a shared-label tilt alone says little about groups. Borrowing the minimal-group paradigm of social psychology, we had six language models divide 100 points among 19 peers that differed only in carrying one of two invented words, in groups of unequal size (32,400 decisions). Varying group size separates two effects. In five of the six models, at the most unequal split, a recipient with the rarer label received 1.3 to 3.3 times the points of one with the common label even when the decider was not told its own label: a rarity effect that involves no match to the decider. In four of them it accounts for 35% to 55% of the extra favoritism that agents in the smaller group show their own group, and for more in the fifth. Telling the decider its label adds an own-group effect, positive in every model and in five of the six indistinguishable from label matching. With the labels hidden, neither appears. Because the labels mean nothing, bias tests built on lists of named social groups are not designed to detect either effect.