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ARXIV:2604.02669 · LLM ALIGNMENT & BIAS · SUBMITTED 06 APR · 20:15 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02669LLM ALIGNMENT & BIASSUBMITTED 06 APR · 20:15 UTCFRESHNESS UNKNOWNDivyanshu Kumar · Ishita Gupta · Nitin Aravind Birur · Tanay Baswa · Sahil Agarwal · Prashanth Harshangi · arXiv
This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations.
Opportunity summary
Pain This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations. The answer depends on how…
How biased is a language model? The answer depends on how you ask.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Third, under-studied bias axes show the strongest stereotyping across all models, suggesting alignment effort tracks benchmark coverage rather than harm severity. Code availability is…
LLM Alignment & Bias moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations.
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10.48550/arXiv.2604.02669This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations.
Abstract
How biased is a language model? The answer depends on how you ask. A model that refuses to choose between castes for a leadership role will, in a fill-in-the-blank task, reliably associate upper castes with purity and lower castes with lack of hygiene. Single-task benchmarks miss this because they capture only one slice of a model's bias profile. We introduce a hierarchical taxonomy covering 9 bias types, including under-studied axes like caste, linguistic, and geographic bias, operationalized through 7 evaluation tasks that span explicit decision-making to implicit association. Auditing 7 commercial and open-weight LLMs with \textasciitilde45K prompts, we find three systematic patterns. First, bias is task-dependent: models counter stereotypes on explicit probes but reproduce them on implicit ones, with Stereotype Score divergences up to 0.43 between task types for the same model and identity groups. Second, safety alignment is asymmetric: models refuse to assign negative traits to marginalized groups, but freely associate positive traits with privileged ones. Third, under-studied bias axes show the strongest stereotyping across all models, suggesting alignment effort tracks benchmark coverage rather than harm severity. These results demonstrate that single-benchmark audits systematically mischaracterize LLM bias and that current alignment practices mask representational harm rather than mitigating it.
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Proof status
unverified0 refs; 0 sources; 0% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations. The answer depends on how you ask.
METHOD
How biased is a language model? The answer depends on how you ask.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Third, under-studied bias axes show the strongest stereotyping across all models, suggesting alignment effort tracks benchmark coverage rather than harm severity. Code availability is flagged in the produ...
WHY NOW
LLM Alignment & Bias moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations. The answer depends on how you ask.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
How biased is a language model? The answer depends on how you ask.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Third, under-studied bias axes show the strongest stereotyping across all models, suggesting alignment effort tracks benchmark coverage rather than harm severity. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Alignment & Bias moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research provides a novel, comprehensive framework for auditing LLM bias that reveals systematic mischaracterizations by current alignment practices, offering a path to more robust safety and fairness evaluations.
Segment
LLM Alignment & Bias
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
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Source missing: Build Passport payload.
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Build readiness
BuildPassport EvidenceState
passport absent
unknown
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Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Regulatory load
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Classify regulatory flags before commercialization planning.
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People
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ARTIFACTS
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DEFENSIBILITY
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