AI Alignment
AI alignment is the study and practice of making artificial intelligence systems behave in ways that accord with intended goals, preferences, constraints, or institutions. The term is used at several levels.
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Showing 1-35 of 35 articles
Algorithmic bias
Algorithmic bias is the tendency of a computer system to produce systematic, repeatable errors that advantage some groups of people over others.
Algorithmic fairness
Algorithmic fairness is the study of how automated decision systems can be made to produce decisions that are equitable across protected attributes such as race, gender, age, religion, and disability.
Automation Bias
Automation bias is the tendency for humans to favor suggestions and outputs from automated decision-making systems over contradictory information from non-automated sources
Bias
Bias in artificial intelligence carries three distinct technical meanings: a learnable scalar parameter added inside a neuron, the systematic error component of an estimator (the "bias" in the bias-variance…
Confirmation Bias
Confirmation bias is the tendency to search for, interpret, favor, and recall information in ways that confirm one's preexisting beliefs, and in artificial intelligence it appears in three main forms: human…
Counterfactual Fairness
Counterfactual fairness is a formal definition of algorithmic fairness rooted in causal inference: a prediction is counterfactually fair toward an individual if it would remain unchanged in a counterfactual…
Coverage Bias
Coverage bias is a type of selection bias that occurs when the method used to collect data systematically excludes part of the target population
Demographic Parity
Demographic parity, also called statistical parity or acceptance rate parity, is a fairness criterion in machine learning that requires a model's predictions to be statistically independent of a protected…
Disparate Impact
Disparate impact is a legal and statistical concept describing situations where a seemingly neutral policy, practice, or algorithm produces disproportionately adverse outcomes for members of a protected class…
Disparate Treatment
Disparate treatment is the intentional, less favorable treatment of an individual because of a protected attribute such as race, gender, age, religion, national origin, or disability, and in machine learning…
Equality of Opportunity
Equality of opportunity is a group-fairness criterion in machine learning that requires a classifier's true positive rate (TPR) to be equal across all groups defined by a sensitive attribute: qualified…
Equalized Odds
Equalized odds is a group fairness criterion in machine learning that requires a classifier's true positive rate (TPR) and false positive rate (FPR) to be equal across all groups defined by a protected…
Experimenter's Bias
Experimenter's bias (also called the observer-expectancy effect, experimenter expectancy effect, or experimenter effect) is a type of cognitive bias in which a researcher's expectations or beliefs about the…
Fairness Constraint
A fairness constraint is an explicit mathematical condition imposed on a machine learning model during training, evaluation, or post-processing that forces its predictions to satisfy a specified group-fairness…
Fairness Metric
A fairness metric is a quantitative, mathematical measure used to evaluate whether a machine learning model's predictions or decisions treat different demographic groups equitably.
Federated Learning
Federated learning is a machine learning technique that trains a shared model across many decentralized devices or servers without moving their raw data to a central location.
Feedback Loop
A feedback loop in machine learning is a cycle in which a deployed model's predictions influence the real world, and the resulting data is then collected and used to retrain the same model
Group Attribution Bias
Group attribution bias is the tendency to assume that what is true of one member of a group is true of the entire group
Human-in-the-loop
Human-in-the-loop (HITL) describes any arrangement in which a person is a required participant in an automated system's operating cycle rather than a bystander to it.
Implicit Bias
Implicit bias is an umbrella term that, in artificial intelligence and machine learning, refers to systematic tendencies operating below the surface of explicit design choices.
In-Group Bias
In-group bias (also called in-group favoritism or in-group preference) is the systematic tendency to favor members of one's own social group over members of other groups
Incompatibility of Fairness Metrics
The incompatibility of fairness metrics (also called the impossibility theorem of fairness or fairness trade-offs) is the proven mathematical result that several widely used definitions of algorithmic fairness…
Individual Fairness
Individual fairness is the principle in machine learning that any two individuals who are similar with respect to a task should receive similar algorithmic outcomes.
Interpretability
Interpretability in artificial intelligence concerns what people can learn about a system's behavior, predictions, or internal computations, and whether that understanding is reliable enough for a stated…
Machine learning terms/Fairness
The key machine learning fairness terms are the formal criteria used to define and measure when a model treats demographic groups equitably, together with the named biases that make models unfair.
Margaret Mitchell (computer scientist)
Margaret Mitchell is an American computer scientist who works on AI ethics, fairness in machine learning, and the documentation of AI systems.
Out-Group Homogeneity Bias
Out-group homogeneity bias, also called the out-group homogeneity effect, is the cognitive bias in which people perceive members of an out-group as more similar to one another than members of their own…
Predictive Parity
Predictive parity is a group fairness metric in machine learning that holds when a classifier's positive predictive value (PPV), also called precision
Predictive rate parity
Predictive rate parity (PRP), also called predictive parity, predictive value parity, or the sufficiency criterion, is a group fairness metric in machine learning that requires a classifier's positive…
Reporting Bias
Reporting bias is a type of data bias in machine learning that occurs when the frequency of events, properties, or outcomes captured in a dataset does not reflect their real-world frequency, because people…
Sampling Bias
Sampling bias is a systematic error in statistics and machine learning that occurs when a sample is collected so that some members of the intended population have a higher or lower probability of being…
Selection Bias
Selection bias is a systematic error that occurs when the data used for analysis, training, or evaluation does not accurately represent the population or domain it is intended to describe
Sensitive Attribute
A sensitive attribute (also called a protected attribute or protected characteristic) is any feature in a dataset that corresponds to a legally or ethically protected personal trait, such as race, sex or…
Unawareness (Fairness Through Unawareness)
Unawareness to a sensitive attribute, more commonly called fairness through unawareness (FTU), is a machine learning fairness approach that tries to make a model fair by simply not giving it the sensitive or…