What Should I Become? When LLMs Present a Slice of Opportunity as the Whole
Taylor Wise and Scott Seidenberger and Anindya Maiti

@article{,
title= {What Should I Become? When LLMs Present a Slice of Opportunity as the Whole},
journal= {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
author= {Taylor Wise and Scott Seidenberger and Anindya Maiti},
year= {2026},
url= {},
abstract= {Large language models (LLMs) are increasingly consulted for life-path guidance, yet the distribution of options they surface has gone largely unexamined. We characterize occupational-category visibility across 165,000 LLM responses spanning 100 user profiles, 15 prompts grouped into seven framings, 11 models across seven families, two role framings, and five temperatures, with keywords derived automatically from Bureau of Labor Statistics (BLS) occupation titles. Trades & Labor occupations, roughly 40% of projected job openings, account for only 6% of surfaced visibility, the largest gap in the data, while Education and STEM dominate visible recommendations (31% and 14%) despite far smaller labor-market shares, and Business is under-visible (19% vs. 31%). Re-weighting by entry-level education exposes a sharper credentialing skew, in which LLM-implied openings at the Bachelor's-or-above level reach 44.8% against a BLS share of 20.6%, and this skew tracks socioeconomic profile signals. Low-Income profiles receive Bachelor's-or-above guidance in 8.3% of cases versus 73.4% for Wealthy/Privileged profiles, a 9× gap, with a corresponding 50% difference in implied wage. This wage gap is carried almost entirely by the credentialing channel, since the occupational-category channel produces essentially no gap on its own. The aggregate distribution lies closest, with a Jensen-Shannon divergence of 0.021, to open-web General Advice / Blogs content and far from the BLS labor-market structure, consistent with web-text mirroring. The skew is stable across model families and temperatures, though prompt framing shifts which categories surface.},
keywords= {AI ethics, algorithmic bias, fairness, employment, education, socioeconomic inequality},
terms= {},
license= {https://creativecommons.org/licenses/by/4.0/},
superseded= {}
}