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Our project investigates gender bias in AI-driven resume screening using mechanistic interpretability techniques. By testing a language model's decision-making process on resumes differing only by gendered names, we uncovered a statistically significant bias favoring male-associated names in ambiguous cases. Using Goodfire’s Ember API, we analyzed model logits and performed rigorous statistical evaluations (t-tests, ANOVA, logistic regression).

Findings reveal that male names received more positive responses when skill matching was uncertain, highlighting potential discrimination risks in automated hiring systems. To address this, we propose mitigation strategies such as anonymization, fairness constraints, and continuous bias audits using interpretability tools. Our research underscores the importance of AI fairness and the need for transparent hiring practices in AI-powered recruitment.

This work contributes to AI safety by exposing and quantifying biases that could perpetuate systemic inequalities, urging the adoption of responsible AI development in hiring processes.

Cite this work:

@misc {

title={

AI Bias in Resume Screening

},

author={

Aliane Inès, Abidal Mauro

},

date={

3/10/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Apr 14, 2025

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Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

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Jan 24, 2025

CoTEP: A Multi-Modal Chain of Thought Evaluation Platform for the Next Generation of SOTA AI Models

As advanced state-of-the-art models like OpenAI's o-1 series, the upcoming o-3 family, Gemini 2.0 Flash Thinking and DeepSeek display increasingly sophisticated chain-of-thought (CoT) capabilities, our safety evaluations have not yet caught up. We propose building a platform that allows us to gather systematic evaluations of AI reasoning processes to create comprehensive safety benchmarks. Our Chain of Thought Evaluation Platform (CoTEP) will help establish standards for assessing AI reasoning and ensure development of more robust, trustworthy AI systems through industry and government collaboration.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.