Discrimination in hiring: What it is and how to prevent it

TL;DR

• Discrimination in hiring refers to any action in the hiring process, from job ads through to offers, that violates laws protecting people on the basis of race, colour, religion, sex or national origin.
• Prevent it by designing a fair and consistent recruitment process, including inclusive job descriptions, structured interviews with objective criteria, documented decisions, and clear and reasonable accommodation procedures.
• Track outcomes by stage and by protected characteristics to spot patterns; investigate outliers and fix the steps that leak.
• Be cautious with technology. AI discrimination in hiring can arise from biased training data or design. Choose ethical AI tools with explainability, bias testing, and strong data governance.
• Keep humans accountable. Train hiring managers, run diverse panels, and separate medical information from decision-making.
• Tools should remove friction, not replace judgment. Sapia.ai supports structured, mobile-first interviews with explainable scoring and real-time scheduling while managers retain final decisions.

A fair hiring system protects both individuals and your organisation. Beyond compliance, a consistent and transparent approach reduces disputes, improves trust with job applicants, and leads to better hires. This guide explains what discrimination in hiring looks like, where it hides in everyday practice, and how to prevent it without slowing your team down.

What “discrimination in hiring” means

In most jurisdictions, it’s unlawful to make hiring decisions based on protected characteristics such as race, colour, religion, sex, gender identity, sexual orientation, national origin, disability, and age. Federal law prohibits employers from discriminating against or taking adverse action against an employee or job applicant based on these protected traits, including race, colour, religion, sex, and other protected categories.

These laws make it illegal to discriminate based on specific protected characteristics in employment practices. In the United States, for example, Title VII of the Civil Rights Act and the Equal Pay Act are key federal laws that prohibit discrimination, alongside the Age Discrimination legislation, the Americans with Disabilities Act (the “disabilities act”), and the Rehabilitation Act (covering entities receiving federal financial assistance and federal contractors, some of whom also require affirmative action).

The Fair Labor Standards Act also plays a crucial role in establishing workplace protections, including minimum wage and overtime standards. The Immigration and Nationality Act bars discrimination based on immigration status and nationality. Oversight is led by the Equal Employment Opportunity Commission (EEOC) — formally, the Employment Opportunity Commission EEOC — which enforces employment discrimination laws covering recruitment, promotion decisions, and pay.

The Rehabilitation Act and the Americans with Disabilities Act provide specific protections for qualified individuals with disabilities, ensuring they have equal employment opportunities and reasonable accommodations. Certain federal laws and executive orders require employers, especially federal contractors, to take affirmative action to promote equal opportunity and remedy past discrimination. Anti-discrimination laws protect both employees and job applicants, and employers have legal obligations to ensure fair treatment and prevent discriminatory practices in the workplace. Statutes like the Age Discrimination in Employment Act also provide protections for older workers, ensuring equal pay and benefits. When federal financial assistance is involved, the primary objective of the assistance is considered in determining whether anti-discrimination laws apply.

Even if your business is outside the US, these terms are widely used and the principles travel. Wherever you operate, align your employment practices with local civil rights laws and seek counsel for edge cases. Nothing below is legal advice; it’s a practical blueprint to remove discriminatory hiring practices from the day-to-day.

Where discrimination appears in the hiring process

The most common risks arise not from intent but from design. Small choices create discriminatory barriers that filter people out before anyone speaks to them. Discriminatory practices can include a range of illegal behaviours, from biased screening to creating a hostile environment.

A brief tour of typical pressure points helps teams spot and fix issues early.

Workplace discrimination is illegal and can arise at any stage of the hiring process if proper safeguards are not in place.

Job descriptions and job ads

Ambiguous criteria, overly high degree requirements, or gender-biased terms narrow the candidate pool unnecessarily. “Culture fit” language can mask hiring bias against diverse backgrounds. If a requirement isn’t job-related and tied to essential functions, don’t include it.

Sourcing and “word-of-mouth recruitment”

Relying on a single network reproduces sameness. Word-of-mouth recruitment inside homogeneous teams may privilege a particular race or school. Use multiple channels to widen reach and avoid excluding job seekers from different communities.

Screens and shortlists

Requests for photos, dates of birth, or personal details unrelated to the role create risk. So does dismissing qualified candidates because of gaps or educational background, rather than capability. Legal protections ensure that applicants are not treated unfairly compared to other applicants, especially regarding compensation discussions, in compliance with Executive Order 11246. Keep early filters tight and relevant.

Interviews and decisions

Unstructured chats invite unconscious bias, confirmation bias, affinity bias, age bias, and proximity bias. Off-topic questions about religious beliefs, plans for children, or related medical conditions are never appropriate. Harassment during interviews can include not only verbal but also physical conduct, which contributes to a hostile environment. Keep notes on objective criteria; avoid relying on “gut feel”.

Pay and offers

Errors here introduce equal pay issues. Anchor offers to a band and objective factors, not to a candidate’s previous pay (which can perpetuate employment discrimination). Document your rationale. Employees performing equal work must receive equal pay, regardless of gender, race, age, or other protected characteristics, in accordance with legal requirements.

AI discrimination in hiring: risks and realities

Technology can help — or harm — depending on design and oversight. AI bias in hiring typically arises from three sources:

• Training data mirrors past patterns (e.g., over-valuing certain schools or “white-associated names” while undervaluing “black-associated names”), leading to discriminatory outcomes.
• Algorithmic bias from model design or proxies (postcode as a stand-in for race or socioeconomic status).
• Poor governance: unclear consent, vague purpose, and no audit trail.

These issues show up in headlines as AI hiring discrimination, AI hiring bias examples, or bias in AI hiring systems. The fix isn’t “no tech”; it’s good tech with guardrails:

• Use AI systems that publish methods, allow review, and support challenges.
• Demand bias testing across legally protected characteristics and regular re-audits.
• Avoid feeding sensitive data into the model; strip irrelevant attributes.
• Keep human judgment in the final decision; treat AI as a decision support tool, not a decision maker.

Sapia.ai is built with these principles: structured, mobile interviews; explainable scoring aligned to your rubric; and visible pass-through by stage so you can spot disparities quickly — while hiring managers remain accountable.

How to prevent discrimination in the hiring process

Preventing issues upfront is simpler than fixing them after a complaint. Here’s a practical sequence any recruitment team can run.

If hiring criteria are based on safety, employers must provide evidence that employees can perform the job safely and consider alternatives to avoid discrimination.

1) Write inclusive job descriptions tied to the work

• Describe outcomes, not personalities.
• Keep specific criteria short and essential; remove degree requirements that don’t affect a person’s ability to do the job.
• Use gender-neutral language and offer adjustments from the start (how to request reasonable accommodation for assessments or interviews).
• State pay ranges and the selection steps to reduce anxiety for applicants.

2) Broaden sourcing and remove discriminatory barriers

• Mix channels: community groups, targeted job boards, returner programmes, and networks that reach underrepresented groups.
• Calibrate recruitment processes to avoid excluding people by scheduling alone (e.g., school runs, religious days).
• Use clear skills signals to attract candidates from diverse backgrounds and non-linear careers.

3) Standardise early screens to reduce unconscious bias

• Anonymise early reviews (“blind recruitment”) so names and schools aren’t shown.
• Replace ad-hoc calls with a short, structured automated first step. Structured interviews with the same questions for everyone, scored against behaviour anchors, are proven to mitigate bias.
• Add a small, job-relevant task (a draft message or prioritisation exercise). This shifts attention to evidence over “similarity”.

Sapia.ai supports this by delivering a mobile, structured first interview scored against your criteria, then booking live steps automatically. It’s one way to reduce hiring bias using AI interview software without losing the human experience.

4) Train hiring managers and panels

• Run short sessions on unconscious bias (including implicit bias), affinity bias, confirmation bias, age bias, beauty bias, and sexual orientation bias — with real, job-specific examples.
• Use diverse hiring committees to bring different perspectives into the room; diversity reduces the risk of poor hiring decisions based on existing beliefs or previous interactions.
• Share a one-page rubric for each role so everyone scores the same behaviours in the same way.

It’s important to remember that while training will assist with awareness, it’s impossible to remove biases from humans. It’s always better to build inclusive hiring systems that prevent human bias than rely on training. 

5) Design a fair interview process, end-to-end

• Tell candidates who they’ll meet and how long it will take.
• Provide reasonable accommodation options by default (adjusted times, alternative formats, extra time where appropriate).
• Keep notes factual and tied to the criteria. Avoid shorthand like “not a fit”.
• Separate all medical information from decisions. If tasks must be done safely, test them without probing into private health.

6) Control pay and offers to avoid discrimination

• Anchor offers to the role’s band and documented skills evidence; don’t use prior pay as your benchmark.
• Check parity regularly to avoid Equal Pay Act problems. Pay and offers should be consistent not only for new hires but also for other employees in similar roles to prevent discrimination.
• Document exceptions explicitly.

7) Govern your AI hiring tools

• Ask vendors for model cards, audit reports, and explainability.
• Require re-testing periodically; log changes and outcomes.
• Define who can access candidate data, for what purpose, and for how long.
• Keep a documented path for a candidate to request human review.

This is how you address AI bias in hiring systems, AI bias in hiring algorithms, and AI bias in hiring practices while enjoying the speed and consistency benefits of modern tools.

How to detect and measure discrimination

You can’t fix what you can’t see. Build light-touch measurement into everyday work.

• Track representation and pass-through rates at each stage of the employee lifecycle, from hiring to promotion and retention. Tracking these metrics helps ensure that all workers are treated fairly and protected from discrimination.

Metrics that matter

• Representation and pass-through by stage: applied → screened → interviewed → offered → hired, by lawful, self-reported demographic groups (e.g., race, national origin, gender, age, disability).
• Time to first interview and time to offer for each group.
• Offer rates, equal pay checks, and early retention (30/90 days).
• Candidate pulse: “Was the process clear and fair?”

If a group starts strong at the application stage but drops sharply at the interview stage, the issue is the step, not the pipeline. Change one variable (questions, panel composition, scoring guidance), re-measure, and document.

Reviewing decisions to avoid employment discrimination

• Keep short decision logs tied to criteria. “Scored 2/5 on customer recovery” is useful; “not our type” is not.
• Hold weekly 20-minute reviews to scan outliers and intent vs outcome.
• Encourage opposing discrimination: make it easy for employees and former employees to raise concerns early and safely.

How to avoid discrimination in hiring: practical examples

Bringing the above together, here’s how to translate policy into behaviour:

• Scenario — interview: A panel member prefers candidates who mirror their university path (anchoring bias). Fix: hide education on the score sheet; focus on work samples and consistent behavioural prompts.
• Scenario — sourcing: Roles filled mainly via internal referrals; the team lacks diversity. Fix: redesign referrals to prompt breadth, widen channels, and track representation by source.
• Scenario — AI screening: A tool downgrades female candidates who attended all-women’s colleges because of skewed training data. Fix: vendor audit, re-sampling of historical data, block use of education as a proxy; add human review.

These moves show how to prevent discrimination in the hiring process without slowing the team.

Documentation and compliance: getting the basics right

Compliance protects candidates and protects your brand.

• Maintain clean records of adverts, criteria, shortlists, scores, and offers.
• Keep genetic information and medical information out of the decision stream.
• For federal contractors or organisations receiving financial assistance from the federal government, understand obligations under affirmative action and the Rehabilitation Act.
• Ensure your people know how to escalate concerns to HR and, where relevant, to the Equal Employment Opportunity Commission. Candidates and employees can also contact the EEOC via e mail for inquiries or to file complaints.

Good documentation is your best evidence of fair, job-related decision-making.

Conclusion

Fair hiring is designed, not improvised. Define objective criteria, widen access, and compare candidates on evidence, not instinct. Use structure — consistent questions, scoring guides, and small work samples — to reduce unconscious biases without adding delay. Measure outcomes by stage to catch issues early, and govern your AI so it helps rather than harms. Over time, you’ll see fewer disputes, faster decisions, and a stronger reputation — because job seekers and employees experience a process that is clear, lawful, and human.

Curious how structured, mobile-first assessments and explainable scoring could help you remove friction and risk? Book a Sapia.ai demo and see how your team can hire faster while reducing discrimination in hiring — with people still in charge of the final call.

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About Author

Kate Young

Head of People Science