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Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

Weighted Mean

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

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Related Experiment Video

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

Exploring the inequitable impact of data missingness on fairness in machine learning.

Sitao Min¹, Hafiz Asif², Jaideep Vaidya¹

¹Rutgers University, Newark, NJ, 07102, USA.

IEEE Intelligent Systems

|June 30, 2025

Summary

Missing data in artificial intelligence (AI) and machine learning (ML) models can worsen fairness disparities, especially when missingness correlates with sensitive attributes. Addressing data gaps is crucial for equitable AI.

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Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

Data-driven models and AI/ML are integral to societal decision-making.
Concerns regarding algorithmic fairness are significant.
The impact of missing data on fairness remains understudied despite its prevalence.

Purpose of the Study:

To systematically evaluate how missing data affects classifier fairness.
To investigate the role of missing data correlated with protected classes and outcomes.

Main Methods:

Analysis of 150 experimental dataset variants reflecting real-world scenarios.
Utilizing a comprehensive framework covering missing data patterns, rates, and mitigation strategies.

Main Results:

Missing data, particularly when correlated with sensitive attributes and outcomes, can exacerbate fairness disparities.
Even small amounts of missingness can significantly impact fairness.
Systemic missingness poses a substantial challenge to equitable AI.

Conclusions:

Addressing missing data is critical for evaluating and ensuring algorithmic fairness.
Fairness evaluations must account for the presence and patterns of missing data.
Mitigation strategies for missing data are essential for developing trustworthy AI systems.