Content
Introduction
This Applied Use Guide provides guidelines and examples for using AI ethically in recruitment processes—from resume screening to candidate matching—while protecting candidates’ rights, promoting diversity, and avoiding unfair gatekeeping.
Reason Why
AI offers efficiency and scalability in vetting large applicant pools. However, historical biases in hiring data can lead AI to reinforce existing inequities—excluding qualified talent. Proactively designing fair and transparent hiring algorithms fosters an inclusive workforce and safeguards your organization’s reputation.
Key Principles
- Transparency: Clearly disclose how AI influences candidate screening or selection, and allow applicants to understand the criteria used.
- Fairness: Actively guard against algorithmic discrimination, ensuring the AI doesn’t penalize candidates for irrelevant factors (e.g., gaps in employment due to caregiving).
- Human Oversight: Keep hiring managers in the loop to interpret AI outputs, override questionable flags, and exercise empathy in final decisions.
- Data Integrity: Train AI on diverse, representative data sets to reduce biases; regularly update or prune stale data that might misrepresent current hiring needs.
- Inclusivity: Evaluate the AI’s impact on underrepresented groups, ensuring that it promotes a wide array of skill sets, backgrounds, and experiences.
Best Practices
- Use AI to Supplement, Not Replace, Human Judgment: Rely on AI for preliminary screening, then have hiring teams confirm results or spot red flags.
- Carefully Evaluate AI Tools Before Implementation: Look for vendors who provide bias audits or can demonstrate that their models meet legal and ethical requirements.
- Monitor the Impact of AI on Diversity and Inclusion: Track how candidate pools evolve, and course-correct if certain demographics are disproportionately filtered out.
- Train Hiring Managers on Ethical AI Use: Equip them with knowledge about how the algorithms work and common pitfalls, so they can interpret outputs responsibly.
Specific Techniques
Technique 1: Candidate Screening
Default Prompt: Screen resumes for the software developer position.
Updated Prompt: Screen resumes for the software developer position in a way that proactively checks for bias. Document how each skill or experience metric is weighed, and ensure that no demographic data (e.g., gender, ethnicity) is used as a criterion.
Technique 2: Candidate Matching
Default Prompt: Match candidates to the open marketing manager role.
Updated Prompt: Match candidates to the open marketing manager role by aligning relevant skills, experiences, and leadership qualities—while ignoring sensitive attributes. Provide a rationale for each match and explain how you avoid unintentional bias in language or cultural references.
Technique 3: Diversity Monitoring
Default Prompt: Provide a report on the diversity of the applicant pool.
Updated Prompt: Provide a diversity report on the applicant pool without revealing sensitive personal data. Highlight any disparities you find, describe how AI overcame potential data bias, and suggest ways to broaden outreach if certain groups are underrepresented.
Technique 4: Feedback Collection Post Interviews
Default Prompt: Collect feedback from candidates about their interview experience.
Updated Prompt: Collect interview feedback while preserving candidate anonymity and preventing retaliation concerns. Explain how AI aggregates and analyzes feedback, indicating any patterns that might reveal biases in the interview process or candidate experience.
