Pith Number

pith:7L2K5X24

pith:2026:7L2K5X24NP3F4KXYGCWWLVT4IW

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AI Alignment Amplifies the Role of Race, Gender, and Disability in Hiring Decisions

Guobin Shen, Michael Thaler, Ze Wang

Post-training alignment amplifies hiring advantages for female and Black candidates by over 300 percent while increasing disadvantages for disabled candidates.

arxiv:2605.13866 v1 · 2026-05-02 · cs.CY · econ.GN · q-fin.EC

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\pithnumber{7L2K5X24NP3F4KXYGCWWLVT4IW}

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Record completeness

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4 Citations open

5 Replications open

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Claims

C1strongest claim

Post-training alignment is the primary driver: relative to matched pre-trained models, alignment amplifies advantages for female and Black candidates by 325% and 330%, and disadvantages for disabled candidates by 171%.

C2weakest assumption

The assumption that the simulated hiring decisions using language models accurately reflect or predict real-world hiring biases without significant influence from the specific prompt designs or model training data distributions.

C3one line summary

Post-training alignment amplifies hiring advantages for female and Black candidates by over 300 percent and disadvantages for disabled candidates by 171 percent, reversing some human discrimination patterns.

References

46 extracted · 46 resolved · 4 Pith anchors

[1] Obermeyer, Z., Powers, B., V ogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science366, 447–453 (2019) 2019

[2] Lambrecht, A. & Tucker, C. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Manage. Sci.65, 2966–2981 (2019) 2019

[3] Caliskan, A., Bryson, J. J. & Narayanan, A. Semantics derived automat- ically from language corpora contain human-like biases.Science356, 183–186 (2017) 2017

[4] Lippens, L., Vermeiren, S. & Baert, S. The state of hiring discrimination: A meta-analysis of (almost) all recent correspondence experiments.Eur. Econ. Rev.151, 104315 (2023) 2023

[5] Wang, Z.et al.Jobfair: A framework for benchmarking gender hir- ing bias in large language models.Findings of the Association for Computational Linguistics: EMNLP 20243227–3246 (2024) 2024

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-17T23:39:19.377412Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

faf4aedf5c6bf65e2af830ad65d67c459452ee49d7338b4dc5dea4dfeb1743cd

Aliases

arxiv: 2605.13866 · arxiv_version: 2605.13866v1 · doi: 10.48550/arxiv.2605.13866 · pith_short_12: 7L2K5X24NP3F · pith_short_16: 7L2K5X24NP3F4KXY · pith_short_8: 7L2K5X24

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7L2K5X24NP3F4KXYGCWWLVT4IW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: faf4aedf5c6bf65e2af830ad65d67c459452ee49d7338b4dc5dea4dfeb1743cd

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "375465d20c6324484a69a08d113d3493292f69770d01cce16ad2faab96e01838",
    "cross_cats_sorted": [
      "econ.GN",
      "q-fin.EC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2026-05-02T16:08:07Z",
    "title_canon_sha256": "6f67c019cedd4b42cc317d15432fd0457213a625838a716dcb336ad0b69de6ef"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13866",
    "kind": "arxiv",
    "version": 1
  }
}