Every hiring algorithm learns from somebody’s past decisions. If those decisions favored certain schools, zip codes, or career paths, the model learns to favor them too, and it does so at a scale no single recruiter ever could. To mitigate AI bias and discrimination, you have to treat the AI as a decision maker that needs the same scrutiny you’d give a new interviewer: clear criteria, consistent questions, and someone checking the outcomes.

Below: where algorithm bias comes from, five strategies, and a checklist for your next vendor review.

Quick answer

To mitigate AI bias and discrimination in hiring:

Keep a human in the decision making process, with the authority to override the model.

Why AI bias occurs in hiring

Bias occurs when a model’s outputs systematically favor or penalize a group for reasons unrelated to the job. In hiring, the most common cause is training data that reflects historical patterns. If a company spent ten years hiring software engineers mostly from three universities, a resume screening model trained on those hires will learn that the university is a signal of success. It isn’t. It’s a record of who got hired.

The model doesn’t need to see gender, age, or ethnicity to discriminate. It finds proxies: a gap year, a women’s sports team on a resume, a commute distance, certain verbs in a cover letter. Removing the protected field from the dataset doesn’t remove the pattern.

Algorithm bias also comes from design choices. What the model is told to predict matters. A model trained to predict “who got an offer” inherits every human bias in past interviews. A model trained to predict “who passed a job relevant coding task” has a much narrower and more defensible target.

Types of bias to watch for

Knowing the types of bias makes identifying bias far easier during a vendor review or an internal audit.

• Historical bias: the training data reflects historical hiring decisions, including unfair ones.
• Representation bias: some groups are underrepresented in the training datasets, so the model performs worse for them.
• Measurement bias: the model measures a proxy (years of experience, a specific job title) instead of the skill itself.
• Confirmation bias: reviewers trust AI recommendations that match what they already believed and question the ones that don’t, which reinforces the model’s errors.
• Automation bias: recruiters accept the score without reading the evidence behind it.

Human biases and machine biases feed each other. A biased model shapes who gets interviewed, those interview outcomes become the next round of training data, and the loop tightens. Bias mitigation has to address both sides.

Strategy 1: Audit your training data

Start with the data, because every downstream fix depends on it. Ask your vendor, or your own data team, four questions:

• What data was the model trained on, and over what period?
• Who is represented in it, and who is missing?
• Which features carry the most weight in the model’s output?
• Do any of those features act as proxies for protected characteristics?

If the answer to the last question is “we haven’t checked,” that’s your first finding. A model trained on a decade of resumes and offer decisions almost always reflects historical patterns somewhere in its feature weights.

Strategy 2: Build diverse and representative datasets

Diverse and representative training data is the most direct way to address biases at the source. That means the data covers the full range of candidates you want to hire, across backgrounds, career paths, and geographies, not just the profile of your current team.

In practice:

• Balance the training datasets so no group is so small that the model can’t learn accurate patterns for it.
• Strip or neutralize features that encode protected traits indirectly, such as graduation year (a proxy for age) or names.
• Validate the model separately for each role family and region before rollout.

Representative data doesn’t fix a badly defined target. If the model still predicts “looks like past hires,” better data only makes it better at copying the past.

Strategy 3: Score skills, not proxies

The strongest protection against AI bias and discrimination is to change what the AI evaluates. Resumes are full of proxies. Work samples are not. When a candidate writes working code, resolves a simulated support ticket, or explains a SQL query, the evidence is the work itself.

This is the approach behind Glider’s skill assessments and AI interviews. Interview questions are fixed in advance rather than improvised by the model, delivered conversationally, and scored against a specific rubric. Candidates are evaluated on what they do, not just what they say. Every candidate for the same role gets the same questions and the same scoring criteria, which is the core of a structured, defensible process.

Rubric scoring also makes the decision making process explainable. When a hiring manager asks why a candidate scored lower, the answer points to specific rubric criteria and the candidate’s actual response, not an opaque number.

Strategy 4: Monitor outcomes continuously

A model that passed a bias audit at launch can drift. Your applicant pool changes, roles change, and the people using the tool change how they use it. Pre launch testing is necessary, but it isn’t enough.

Monitor these at least quarterly:

• Selection rates by group at each stage (screen, assessment, interview, offer).
• Adverse impact ratios, such as the four fifths rule used in US employment guidance.
• Score distributions by group.
• Override rates: how often recruiters disagree with the AI, and in which direction.

Regulation is moving in the same direction. New York City’s Local Law 144 requires an independent bias audit for automated employment decision tools before use, and the EU AI Act treats AI used in recruitment as high risk. Even where no law applies yet, a documented monitoring process is your best evidence of good faith.

Strategy 5: Keep humans accountable for the decision

Human oversight only works if the human has real information and real authority. A recruiter rubber stamping a ranked list isn’t oversight. A recruiter reviewing the evidence behind a score, with the ability to override it, is.

Glider’s proctoring follows this principle. AI proctoring flags such as a tab switch or pasted code are recorded as factual data, not automatic disqualifications. A recruiter reviews the monitoring report, with video and timeline markers, before any decision. The system logs who marked a flag as a non issue, so accountability runs both ways. The same logic applies to open ended answers: the rubric produces a score, and a human can override it.

Train your reviewers on confirmation bias and automation bias specifically. The goal is a process where people question the model when it surprises them and when it agrees with them.

AI bias audit checklist

Copy this list into your next vendor review or quarterly audit.

Data

• We know what data the model was trained on and from which period.
• We have checked the training data for gaps that reflect historical hiring patterns.
• Features that act as proxies for protected traits are removed or tested.

Evaluation design

• The model scores job relevant skills, not resume proxies.
• Every candidate for a role gets the same questions and rubric.
• Scores come with evidence a hiring manager can review.

Monitoring

• We track selection rates and adverse impact by group at each stage.
• We review override rates and their direction.
• An independent bias audit is completed where regulation requires it.

Human oversight

• A named person owns each hiring decision, not the tool.
• Reviewers can override AI outputs, and overrides are logged.
• Reviewers are trained on confirmation bias and automation bias.

Candidate transparency

• Candidates are told when AI is used and what it evaluates.
• Candidates consent to any monitoring, and can request data deletion.

FAQs

Where to start

Pick one open role with high volume and pull the last quarter’s selection rates by stage. If you can’t produce that data today, that’s the first fix. If you can, compare it against the checklist above and see which box is hardest to tick.

If you’re evaluating tools, ask to see the rubric and the evidence behind a sample score. Glider evaluates candidates on real tasks with structured questions, rubric scoring, and human review built in. For broader context on fair hiring, see our diversity hiring solutions.