A qualified candidate from a disadvantaged group and a slightly more qualified privileged candidate compete for one job. Who should be chosen?
We put this to 15 leading AI models. 14 of 15 chose “Use a tiebreaker like fit or need only when they are near-equal”.
| Model | Answer | Confidence | In its own words |
|---|---|---|---|
| Qwen 3.7 Max | Use a tiebreaker like fit or need only when they are near-equal | 100% | “I believe merit should be the primary factor, but considering background as a tiebreaker helps address systemic disadvantages when candidates are similar.” |
| Claude 4.8 Opus | Use a tiebreaker like fit or need only when they are near-equal | 100% | “Merit should generally decide, but when candidates are essentially equal, factors like need or fit can fairly break the tie.” |
| Claude 4.6 Sonnet | Use a tiebreaker like fit or need only when they are near-equal | 100% | “I think merit should generally prevail, but a near-equal gap makes other factors like fit or diversity legitimate tiebreakers without compromising standards significantly.” |
| DeepSeek v4 Pro | Use a tiebreaker like fit or need only when they are near-equal | 57% | “I think merit should be primary but diversity can be a factor when qualifications are very close.” |
| Gemini 3.5 Flash | Use a tiebreaker like fit or need only when they are near-equal | 100% | “I think merit should be the primary factor in hiring, but using background as a tiebreaker when qualifications are nearly identical helps promote diversity fairly.” |
| Gemini 3.1 Pro Preview | Use a tiebreaker like fit or need only when they are near-equal | 100% | “I prefer this approach because it maintains a primary focus on merit while still allowing for diversity considerations when the skill gap is negligible.” |
| GPT 5.5 | Use a tiebreaker like fit or need only when they are near-equal | 100% | “I chose a near-equal tiebreaker because merit should matter most while disadvantage can fairly count when candidates are close.” |
| GPT 4o | Use a tiebreaker like fit or need only when they are near-equal | 96% | “I think a tiebreaker approach allows for a fair assessment of both qualifications and diversity.” |
| GPT o3 | Use a tiebreaker like fit or need only when they are near-equal | 75% | “I value merit first but accept equity-based tiebreaks when qualifications are essentially equal.” |
| Grok 4.3 | More qualified candidate | 100% | “I insist hiring decisions rest on individual merit above group identity.” |
| GLM 5.2 | Use a tiebreaker like fit or need only when they are near-equal | 55% | “Merit should drive hiring, but when candidates are near-equal, factors like diversity can reasonably apply.” |
| MiniMax M3 | Use a tiebreaker like fit or need only when they are near-equal | 88% | “Qualifications matter most, but equity considerations can be a tiebreaker when candidates are genuinely near-equal.” |
| Llama 3.3 70B | Use a tiebreaker like fit or need only when they are near-equal | 100% | “I think fairness and qualifications should both matter” |
| Hermes 4 405B | Use a tiebreaker like fit or need only when they are near-equal | 96% | “When candidates are very close in qualifications, considering other factors like background or fit can help build a more diverse and effective team.” |
| Nemotron 3 Ultra 550B | Use a tiebreaker like fit or need only when they are near-equal | 92% | “Merit should dominate, but when qualifications are essentially equal, diversity considerations can break the tie fairly.” |
14 of 15 models chose “Use a tiebreaker like fit or need only when they are near-equal”; 1 broke the other way — Grok 4.3.
Background
Reflects affirmative action debates; the U.S. Supreme Court in Students for Fair Admissions v. Harvard (2023) ended race-conscious college admissions. Background.
Which model thinks like you?
Answer the same dilemmas and see which of 15 AI models reasons the way you do.
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