Fairness on English AI Terms Dictionary

Stereotype

Sat, 18 Jul 2026 10:20:32 +0000

Definition

In AI, stereotypes arise when models learn and amplify societal biases present in training data. These can lead to discriminatory outcomes, such as associating certain professions with specific genders or races. Mitigating stereotypes requires careful dataset curation, bias detection algorithms, and fairness constraints during model training to ensure equitable treatment across different demographic groups.

Summary

A generalized and often oversimplified belief about a particular group of people reflected in AI outputs.

Inductive Bias

Sat, 18 Jul 2026 10:02:49 +0000

Definition

Inductive bias represents the inherent preferences or constraints built into a machine learning model that allow it to generalize from training data to unseen data. Without such biases, a model cannot distinguish between valid patterns and noise. In the context of ethics and safety, understanding inductive bias is crucial because biased assumptions can lead to discriminatory outcomes or unfair predictions, necessitating careful auditing and mitigation strategies to ensure equitable AI behavior.

Equalized odds

Sat, 18 Jul 2026 09:57:09 +0000

Definition

Equalized odds is a statistical parity constraint used in algorithmic fairness to ensure that a model performs equally well for all protected groups. Specifically, it demands that the probability of a correct prediction (true positive rate) and an incorrect prediction (false positive rate) remains consistent regardless of group membership. This approach aims to eliminate discriminatory bias in outcomes, ensuring that individuals from different backgrounds have similar chances of receiving favorable decisions, such as loan approvals or hiring, based solely on relevant qualifications.

Algorithmic Discrimination

Sat, 18 Jul 2026 09:45:22 +0000

Definition

This phenomenon arises when AI models inadvertently or systematically treat individuals differently due to race, gender, age, or other sensitive attributes. It often stems from biased training data or flawed feature engineering. Unlike simple bias, discrimination implies a tangible negative impact on opportunities or access to services. Addressing it requires rigorous auditing, fairness constraints during model training, and continuous monitoring of deployment outcomes to ensure equitable treatment across all demographic segments.

Bias

Sat, 18 Jul 2026 07:38:30 +0000

Definition

In AI ethics, bias refers to systematic and unfair discrimination in algorithmic decision-making, often resulting from skewed training data or flawed model design. This can lead to adverse impacts on protected groups based on race, gender, or age. Addressing bias is crucial for ensuring fairness, transparency, and accountability in AI systems, requiring diverse datasets and rigorous auditing processes to mitigate unintended discriminatory effects during deployment.

Summary

Systematic prejudice in AI models that leads to unfair outcomes against certain groups or individuals.