A Glossary of Key Terms in Bias & Fairness Metrics in AI

As AI adoption accelerates within HR and recruiting—from resume parsing to candidate screening and predictive analytics—it brings immense opportunities for efficiency and insight. However, it also introduces critical considerations around fairness and ethical deployment. Understanding the terminology associated with bias and fairness in AI is not just a technical exercise; it’s fundamental for HR and recruiting professionals to build equitable processes, mitigate risks, and ensure responsible talent acquisition. This glossary provides essential definitions to help you navigate the complexities of AI ethics, fostering a deeper understanding of how to implement AI systems that uphold fairness and promote diversity.

Algorithmic Bias

Algorithmic bias occurs when an AI system produces outcomes that are systematically prejudiced against certain groups, often due to biased data or flawed design. This can manifest in subtle or overt ways, leading to unfair or discriminatory results. For HR professionals, understanding algorithmic bias is crucial when evaluating AI tools for candidate screening or promotion recommendations. An AI-powered resume parser trained on historical hiring data, for instance, might inadvertently deprioritize qualified candidates from underrepresented groups if past hiring practices exhibited biases, thus perpetuating those biases in current searches. Regular audits and diverse training datasets are essential to mitigate this risk.

Fairness Metrics

Fairness metrics are quantitative measures used to assess how equitably an AI system treats different groups or individuals. These metrics help identify and quantify disparities in outcomes, providing data-driven insights into an algorithm’s performance across various demographics. In the HR context, a fairness metric might evaluate if candidates from different gender or racial groups have an equal probability of being recommended for an interview, even if their raw scores differ slightly. HR teams can leverage these metrics to audit their AI recruitment tools, ensuring alignment with diversity and inclusion goals and making informed adjustments to foster equitable talent acquisition.

Disparate Impact

Disparate impact occurs when a seemingly neutral policy, practice, or algorithmic decision disproportionately affects a protected group, even in the absence of intentional discrimination. This is a critical concept in employment law and applies directly to AI in HR. For example, an AI system that prioritizes candidates who attended specific elite universities might unintentionally create a disparate impact against candidates from less affluent backgrounds or diverse institutions. HR leaders must analyze the actual outcomes of AI tools, not just their intended purpose, to proactively identify and rectify any practices that could lead to indirect discrimination and hinder diverse talent pipelines.

Protected Attributes

Protected attributes are characteristics of an individual that are legally protected from discrimination under various anti-discrimination laws (e.g., Title VII in the U.S.). These typically include race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, and genetic information. When developing or implementing AI for recruiting, it is vital to ensure that these protected attributes are not directly or indirectly used in ways that lead to discriminatory hiring or promotion decisions. Automation should focus on skills, experience, and objective qualifications, while strictly avoiding models that can infer or act upon protected characteristics in a biased manner.

Proxy Discrimination

Proxy discrimination occurs when an AI system uses seemingly neutral features or data points that are highly correlated with protected attributes to indirectly discriminate. The AI might not explicitly use a protected characteristic like race, but it uses a data point that acts as a “proxy” for it. For example, if a hiring AI disproportionately favors candidates who live in specific zip codes, and those zip codes correlate strongly with a particular racial or socioeconomic group, it could lead to proxy discrimination. HR professionals must diligently vet AI models to ensure that even seemingly innocuous data points aren’t inadvertently serving as proxies for prohibited discriminatory factors, thereby maintaining fairness and compliance.

Data Bias

Data bias refers to inaccuracies, imbalances, or systemic errors within the data used to train an AI model, which then lead to biased model outcomes. Since AI learns from the data it consumes, any inherent biases in that data will be reflected, and often amplified, in the model’s decisions. In HR, an AI tool trained exclusively on historical data from a predominantly male tech workforce might exhibit data bias if later used to screen candidates for roles requiring diverse skill sets or greater gender representation. Cleaning, diversifying, and regularly auditing training data is paramount to prevent the perpetuation of historical inequalities in automated recruiting processes.

Historical Bias

Historical bias is a specific form of data bias embedded in training data due to past societal or organizational practices that favored certain groups over others. This means that if an organization historically hired very few women into leadership roles, an AI trained on that historical hiring data might learn that “successful leader” attributes are predominantly male-coded, thereby discriminating against equally qualified female candidates. Overcoming historical bias requires conscious efforts to not only diversify future training data but also to actively identify and correct for past disparities, often through re-weighting or augmenting datasets to represent desired equitable outcomes.

Measurement Bias

Measurement bias occurs when there are systematic differences in how a feature, variable, or outcome is measured or perceived across different demographic groups. This can lead to unfair comparisons or evaluations by an AI system. For instance, if an AI-powered assessment tool uses language, cultural references, or problem-solving scenarios that are significantly more familiar or relevant to one demographic group than another, it could introduce measurement bias. This unfairly disadvantages certain candidates who may be equally competent but perform poorly due to unfamiliarity with the assessment’s context. HR teams must ensure assessment tools are culturally fair and validated across diverse populations to provide accurate and equitable evaluations.

Selection Bias

Selection bias is introduced when the data used to train an AI model is not truly representative of the population the model will be applied to or of the full range of possibilities it might encounter. This often happens when data is collected from a non-random or incomplete sample. An AI model trained exclusively on data from successful sales hires in North America, for example, might exhibit selection bias if deployed globally without adjustment, potentially misidentifying suitable candidates in other regions with different cultural or professional norms. Ensuring training data accurately reflects the target population and diverse contexts is critical for universal fairness and effective AI performance.

Group Fairness

Group fairness refers to a set of fairness criteria that aims to ensure an AI system performs equally well or yields similar outcomes across predefined demographic groups. Rather than focusing on individuals, it assesses equality at the group level. This could mean ensuring that a resume screening algorithm has a similar false positive rate (incorrectly identifying a poor fit) and false negative rate (missing a good fit) for both male and female applicants, or for different racial groups. HR should monitor group fairness metrics to prevent systemic exclusion or disadvantage for any particular segment of the applicant pool, ensuring equitable opportunities across all demographics.

Individual Fairness

Individual fairness is the principle that similar individuals should be treated similarly by an AI system, regardless of their group affiliation. It focuses on ensuring that an algorithm doesn’t make disparate decisions between two individuals who are alike in all relevant aspects. If two candidates have nearly identical qualifications, experience, and performance indicators, an individually fair AI system should assign them very similar scores or recommendations, irrespective of their gender, age, or ethnicity. Achieving individual fairness is often more challenging than group fairness but is crucial for preventing any form of arbitrary discrimination against specific individuals within groups, upholding ethical decision-making.

Transparency (in AI)

Transparency in AI refers to the ability to understand how an AI system works, the data it uses, and the reasoning behind its decisions. This clarity is essential for building trust and accountability, especially in sensitive applications like HR and recruiting. For an AI-driven candidate ranking system, transparency means being able to explain why a particular candidate was ranked higher than another, beyond just stating a score. HR professionals need transparent AI tools to justify hiring decisions, build trust with candidates by explaining outcomes, and meet regulatory requirements, ensuring that automated processes aren’t perceived as opaque “black boxes.”

Explainable AI (XAI)

Explainable AI (XAI) is a field of AI development that focuses on creating models whose outputs can be readily understood and interpreted by humans, thereby promoting transparency and trust. Unlike traditional “black box” AI, XAI tools can provide insights into *why* an AI made a particular decision. In the HR context, XAI can highlight specific keywords, experiences, or patterns in a resume that led the AI to flag an applicant for an interview. This helps HR professionals validate the AI’s logic, identify potential biases in its reasoning, and provide concrete, data-backed feedback to candidates or internal stakeholders, moving beyond opaque algorithmic decisions to explainable insights.

Accountability (in AI)

Accountability in AI refers to the ability to assign responsibility for the outcomes and impacts of an AI system, particularly in cases of errors, biases, or unintended negative consequences. As AI becomes more autonomous, defining clear lines of accountability becomes paramount for ethical deployment. In recruiting, accountability means knowing who is responsible if an AI system leads to discriminatory hiring practices—whether it’s the HR team implementing it, the vendor providing it, or the developers who built it. Establishing clear governance frameworks, ethical oversight committees, and robust auditing processes for AI tools ensures that ethical considerations and fairness are integrated from design to deployment, with clear ownership for results.

Calibration

Calibration, as a fairness metric, assesses whether the predicted probabilities from an AI model are accurate across different demographic groups. Specifically, it checks if, for example, when an AI predicts a 70% chance of success for a candidate, approximately 70% of candidates with that prediction actually succeed, and crucially, if this holds true for both male and female candidates, or candidates from different ethnic backgrounds. In recruiting, proper calibration ensures that the AI’s confidence levels and predictive accuracy are equally reliable and consistent across all applicant segments. This prevents situations where the AI might be overconfident for one group and underconfident for another, leading to biased decision-making.

If you would like to read more, we recommend this article: Protecting Your Talent Pipeline: The HR & Recruiting CRM Data Backup Guide