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LLM Linguistics Auditor

Job No: SCTDST001

Location: Brisbane

Employment Status: Full-time

Closing Date: 31 Jan 2025 AEST

We are seeking an AI-Powered Recruitment Algorithm Auditor for Underrepresented Dialects to ensure that our AI-driven recruitment systems fairly assess candidates who speak niche or underrepresented dialects in English and other languages. This role will involve evaluating our Natural Language Processing (NLP) models against real-world hiring scenarios to identify and mitigate linguistic biases in AI-driven recruitment decisions.

Key Responsibilities:

Analyze AI recruitment decisions for linguistic biases affecting speakers of dialects such as ESL, Singlish, Scots, AAVE, and lesser-known regional variations.
Train AI models to better interpret nuanced phrases, idioms, and speech patterns that may be misclassified as "non-standard" or "unprofessional."
Conduct bias audits of chatbot interactions, video interview assessments, and voice recognition software.
Work with linguists, data scientists, and compliance teams to propose solutions that improve fairness in AI hiring.
Design and run usability tests with real candidates who speak these dialects to assess algorithmic fairness.
Develop reports and recommendations for compliance with diversity hiring standards.

Qualifications:

PhD or Master’s in Computational Linguistics, Sociolinguistics, AI Ethics, or a related field.
Extensive knowledge of underrepresented dialects and their linguistic structures.
Strong proficiency in Python, NLP libraries (e.g., spaCy, NLTK, Hugging Face Transformers), and AI fairness frameworks.
Experience in AI auditing or bias mitigation in recruitment technologies.
Ability to recruit and work with speakers of niche dialects for live testing.
Strong communication skills and ability to work cross-functionally with engineers and DEI (Diversity, Equity, and Inclusion) teams.

Nice-to-Have:

Experience with voice recognition technology and speech-to-text models.
Previous work in HR tech, talent acquisition AI, or compliance auditing.
Multilingual proficiency in languages with heavy dialectical variance (e.g., Arabic, Chinese, Hindi).

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Questions

What strategies would you employ to train AI models to better understand and interpret nuanced phrases, idioms, and speech patterns from speakers of niche dialects? Please elaborate on your approach and any relevant tools or frameworks you would use.

Describe your experience with underrepresented dialects and how you have applied this knowledge to identify linguistic biases in AI systems. Please provide specific examples of projects or audits you have conducted.

Discuss a time when you worked with a cross-functional team, including linguists and data scientists, to address AI bias in recruitment technologies. What was your role, and what were the outcomes of your collaboration?

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