How to Assess Your AI Tools for Bias (Especially If You’re Using Large Language Models)
If you’re looking to stay on the good side of US and Canadian anti-discrimination laws, start by giving your AI a thorough checkup. Whether you’re using a custom model, something built on large language models like OpenAI, or a hybrid approach, here’s a practical checklist to ensure your tool doesn’t inadvertently discriminate—or land you in hot water.
1. Data Sources: Where Does Your AI Learn From?
- Historical Internet Data: Large language models (e.g., GPT) ingest vast amounts of text from the internet. This can contain historical hiring biases.
- Company-Specific Datasets: If you feed your AI past hiring data that might reflect biased decisions, you risk perpetuating those biases.
- PII Stripping: Check that you’re not feeding the model personal identifiers like names, photos, or demographic details.
Key Question
Does your AI rely on data that might be riddled with biases (internet text, old hiring data) without any filtering or cleaning?
2. The AI’s Actual Role: Who’s Calling the Shots?
- Full Automation vs. Human Oversight: If your AI is auto-rejecting candidates, it’s more likely to face regulatory scrutiny.
- Categorization vs. Decision-Making: Tools that only organize or label data (e.g., job titles, skill sets) generally pose fewer compliance risks.
- Transparency: Can you explain how the system arrived at a particular match or score?
Key Question
Is the AI merely assisting (sorting, categorizing, or summarizing) or outright deciding who’s hired and who’s not?
3. Matching Resumes to Job Descriptions: Potential Bias Pitfalls
- Large Language Model “Knowledge”: When using GPT-like services to compare a résumé against a job description, remember that these models can reflect societal biases in their training data.
- Objective Criteria: Ensure the model focuses on quantifiable factors—like skills, tenure, and relevant experience—rather than intangible (and potentially biased) signals.
- Prompt Engineering: If you’re prompting a model like OpenAI to match candidates, design prompts that exclude demographic info and emphasize objective criteria.
Key QuestionHave you defined strict, unbiased prompts or instructions so that the AI compares résumés and job descriptions fairly, without letting stereotypes or historical prejudice slip in?
4. Mitigating Bias: Human-in-the-Loop Checks
- Manual Review: Always have a human confirm final decisions. The less your AI is left to “trust its gut,” the safer you are.
- Flagging Suspicious Results: If the AI’s scoring or ranking seems off (e.g., a well-qualified candidate gets ranked low), investigate.
- Explainability: Provide a clear rationale—“Candidate A has X years of Java experience vs. Candidate B’s 2 years”—not “the AI just felt it.”
Key Question
Are there clearly defined steps for humans to intervene, question, or override the AI’s suggestions?
5. Auditing & Documentation
- Regular Bias Tests: Run monthly or quarterly checks on how the AI ranks diverse candidate sets.
- Categorization Accuracy: Especially if your AI is labeling job titles or skills, ensure it’s not mixing up roles or industries in ways that could harm candidates.
- Record Keeping: Document training data sources, prompting strategies, model updates, and any flagged bias incidents. Regulators appreciate a clear paper trail.
Key Question
Can you show regulators an audit log that demonstrates you’re actively monitoring for and correcting bias?
Additional Tips for US & Canadian Compliance
- US:
- Title VII & EEOC: Be ready to demonstrate that your AI doesn’t adversely affect any protected group.
- State Laws: Keep tabs on new regulations in states like Illinois or New York, which have more stringent AI audit requirements.
- Canada:
- Human Rights Acts & Charter: Any data usage that discriminates against protected groups can trigger issues.
- Bill C-27: Emphasizes transparency, accountability, and fair AI processes; your audits and documentation will matter here.
Conclusion: Be Proactive, Not Reactive
Taking a “plug-and-play” approach to AI in hiring can be risky, especially if you’re using large language models that ingest historic bias from the internet. By systematically evaluating your data sources, clarifying the AI’s role, engineering prompts for fairness, and running ongoing audits, you’ll be well-equipped to comply with both US and Canadian regulations. Plus, you’ll build a hiring process that’s genuinely fair— and that’s good business, no matter which side of the border you’re on.
