| |Redline — HR Interview Question Compliance Checker | |
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| Category: HR , Model: Llama-3.3-70B-Instruct (full finetune) + Llama-4-Scout-17B-16E-Instruct (LoRA) — two architectures trained and compared |
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| Problem |
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| There is no public dataset for interview-question legal risk — companies that have asked risky interview questions have no incentive to publish them, and existing public guidance is unstructured listicle advice with no jurisdiction awareness. India and US employment law diverge in specific, checkable ways (e.g. asking prior salary is routine in India but banned in many US states/cities), and no existing tool reasons about that difference rather than pattern-matching on trigger words. |
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| Solution |
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| Given a job description, jurisdiction, and interview question, the model returns a risk classification (safe / legally_risky / bias_prone), the specific risk category, the legal basis (citing Title VII, ADEA, ADA, GINA, IRCA, Maternity Benefit Act 1961, POSH Act 2013, Rights of Persons with Disabilities Act 2016, Constitution Art. 15/16/17, NALSA v. UOI, Navtej Singh Johar v. UOI), a plain-language justification, and a safer reframe where applicable. Every label traces back to a hand-built legal reference table (30 categories, 15 per jurisdiction), not free-form LLM judgment. The seed set deliberately includes hard negatives — questions that sound risky but are legitimate when properly scoped (e.g. an ADA-compliant "can you lift 50 lbs, with or without accommodation" question). |
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| Dataset |
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| Primary seed (36 hand-verified rows) expanded to ~21,000 rows via AutoScientist's synthetic trajectory generation. A second, hand-written 40-row dataset was also used for training. A separate, genuinely held-out evaluation set (eval_set_v2, 30 rows, verified zero overlap with any training data) includes deliberate India/US jurisdiction flip-cases — the identical question posed in both countries, with different correct verdicts (e.g. salary-history questions: safe in India, legally risky in much of the US) — testing genuine cross-jurisdiction reasoning rather than keyword matching. |
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| Results |
| Model On-dataset win rate HR-category win rate Data quality grade |
| Llama-3.3-70B, full FT 25 -> 75 18 -> 82 C -> B (+40.0%) |
| Llama-4-Scout-17B, LoRA 28 -> 72 21 -> 79 D -> B (+117.5%) |
| Limitations |
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| Not legal advice — the reference table should be reviewed by qualified counsel before production use. Covers only India and US employment law. |
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| Assets |
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| Dataset cards (both training datasets + held-out eval), model card, live Gradio Space demo — all built. |
| annotations_creators: [] |
| language - |
| - en |
| language_creators: [] |
| license- [] |
| multilinguality: |
| - monolingual |
| pretty_name- 'adaption-interview_question_samples' |
| size_categories: |
| - 10K<n<100K |
| source_datasets- |
| - 'original' |
| tags: |
| - adaption |
| - instruction-tuning |
| - hr |
| - career-workplace |
| - other |
| task_categories- [] |
| task_ids: [] |
| --- |
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|  |
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| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - hi |
| task_categories: |
| - text-classification |
| tags: |
| - hr |
| - compliance |
| - legal |
| - india |
| - united-states |
| - autoscientist-challenge-2026 |
| pretty_name: HR Interview Question Compliance (Second Training Set) |
| size_categories: |
| - n<1K |
| --- |
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| # HR Interview Question Compliance — Second Training Dataset |
| ### (`interview_question_samples`) |
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| ## Dataset Summary |
| A 40-row hand-written dataset of interview questions labeled for legal/bias risk across |
| India and the US, used as a second training set for |
| `adaption_llama_4_scout_17b_16_interview_question_sampl_1d90ba56` |
| (Llama-4-Scout-17B-16E-Instruct, LoRA) via Adaption's AutoScientist. Hand-written rather |
| than synthetically expanded, this set adds high-precision, human-verified examples |
| alongside the larger synthetically-expanded primary training set |
| (`hiring_question_compliance`). |
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| All generalization and benchmark claims for this project are reported against a separate, |
| independently held-out evaluation set (`eval_set_v2.jsonl`, 30 rows, zero overlap with any |
| training data) — see that dataset's card for details. |
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| ## Schema |
| Same schema as the primary training dataset (`hiring_question_compliance`): |
| `job_description`, `question`, `jurisdiction`, `risk_label`, `risk_category`, |
| `legal_basis`, `justification`, `reframe`, plus an `id` field and a `flip_case_group` |
| field marking rows that form deliberate India/US jurisdiction flip-case pairs (identical |
| question, different correct verdict by jurisdiction). |
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| ## Composition |
| - 40 rows, 19 US / 21 India (approx.) |
| - Includes 5 jurisdiction flip-case pairs |
| - Same legal reference table grounding as the primary training dataset |
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| ## License |
| CC BY 4.0 |
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| ### Domain |
| - Hr (48%) |
| - Career-workplace (15%) |
| - Other (8%) |
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| ### Language |
| - English (100%) |
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| ### Tone |
| - Professional (63%) |
| - Informal (15%) |
| - Respectful (5%) |
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| ### Evaluation Results |
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| - **Quality Gains:** |
| <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/3fa8da93-7f45-4a52-8652-0a6abb1a332c.png" alt="QualityGains" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" /> |
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| - **Grade Improvement:** |
| <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/3dd886ce-a3df-4805-8bc0-c588fcc9e322.png" alt="Grade" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" /> |
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| - **Percentile Chart:** |
| <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/7ff91766-4c81-43ac-a8f3-62062de827b5.png" alt="Percentile Chart" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" /> |
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