Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the...
Background: Machine learning (ML) has been widely adopted in decision-making, making fairness a central ethical and scientific priority. We developed the Themis chatbot, a Large Language Model (LLM) system designed to explain concepts of ML fairness...
The majority of current approaches for bias and fairness identification or mitigation in machine learning models are applications for a particular issue that fails to account for the connection between the application context and its associated sensi...
This study presents a fairness-aware framework for modeling the Probability of Default (PD) in individual credit scoring, explicitly addressing the trade-off between predictive accuracy and fairness. As machine learning (ML) models become increasingl...
As organizations increasingly turn to machine learning for customer segmentation and targeted marketing, concerns about fairness and algorithmic bias have become more urgent. This study presents a comprehensive fairness audit and mitigation framework...
This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Ove...
Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairne...
Machine learning has transformed consumer credit scoring, delivering substantial gains in predictive accuracy over traditional scorecards—but whether those gains come at a cost to fairness has remained contested. The dominant assumption in the...
18 October 2023
The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing b...
20 September 2024
Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classif...
As organizations lean more heavily on their IT systems, managing cyber risk is gaining increasing importance. Organizations are often challenged to determine which cybersecurity risk framework they should adopt. Choosing the right framework can have...
The paper investigates the application of advanced machine learning (ML) methodologies, with a particular emphasis on state-of-the-art deep learning models, to predict financial market dynamics and maximize profitability through algorithmic trading s...
Zooplankton is a fundamental group in aquatic ecosystems representing the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations in populations with environmental change, especi...
Machine learning intrusion detectors are accurate but opaque, so explainable AI justifies their alerts and bias mitigation evens out detection across imbalanced categories. These are usually studied independently, and whether making a detector fairer...
Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learni...
Machine learning-based systems are increasingly deployed in high-stakes domains, such as healthcare, finance, law, and e-commerce, where their predictions directly influence critical decisions. Although these systems offer powerful data-driven suppor...
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study exami...
Machine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many pri...
Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model trained on c...
Fairness is becoming indispensable for ethical machine learning (ML) applications. However, it remains a challenge to identify unfairness if there are changes in the distribution of underlying features among different groups and machine learning outp...
Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical a...
Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling dat...
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian proc...
Machine learning algorithms have become common in everyday decision making, and decision-assistance systems are ubiquitous in our everyday lives. Hence, research on the prevention and mitigation of potential bias and unfairness of the predictions mad...
This manuscript outlines a viable approach for training and evaluating machine learning systems for high-stakes, human-centered, or regulated applications using common Python programming tools. The accuracy and intrinsic interpretability of two types...
Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dim...
The development of algorithms, based on machine learning techniques, supporting (or even replacing) human judgment must take into account concepts such as data bias and fairness. Though scientific literature proposes numerous techniques to detect and...
Probability calibration is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias remains under-explored. In this study, the role of probability calibration tech...
Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP a...
The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are inc...
Depression among students has emerged as a critical issue within educational institutions, leading to the need for approaches that can support early identification of students at risk. This study developed an explainable machine learning framework fo...
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–the...
The increasing availability of free-text components in electronic medical records (EMRs) offers unprecedented opportunities for machine learning research, enabling improved disease phenotyping, risk prediction, and patient stratification. However, th...
Machine learning-based decision support systems bring relief and support to the decision-maker in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems faci...
Customer churn prediction has become crucial for businesses, yet it poses significant challenges regarding privacy preservation and prediction accuracy. In this paper, we address two fundamental questions: (1) How can customer churn be effectively pr...
Artificial intelligence (AI) systems are increasingly embedded in decisions that affect health, employment, housing, justice, and social policy. Rather than neutral tools, they reflect the data on which they are trained, the objectives they optimize,...
Evaluating whether clinical classifiers perform equitably across patient subgroups is a central requirement for the responsible deployment of machine learning in medicine. Conventional approaches test one fairness metric at a time, such as sensitivit...
Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematical...
Background: The development and application of machine learning (ML) methods have become so fast that almost nobody can follow their developments in every detail. It is no wonder that numerous errors and inconsistencies in their usage have also sprea...
Student admission prediction is a crucial aspect of academic planning, offering insights into enrollment trends, resource allocation, and institutional growth. However, traditional methods often lack the ability to address fairness and transparency,...
The Mediterranean Sea, as one of the world’s most climate-sensitive regions, faces significant environmental changes due to rising temperatures. Zooplankton communities, particularly copepods, play a vital role in marine ecosystems, yet their d...
The increasing use of new data sources and machine learning models in transport modelling raises concerns with regards to potentially unfair model-based decisions that rely on gender, age, ethnicity, nationality, income, education or other socio-econ...
Objectives: Anticipated telehealth use is an important determinant of whether telehealth can function as a durable component of hybrid care models. However, there are limited practical tools to identify patients at risk of discontinuing telehealth. W...
Background: Warfarin’s narrow therapeutic index requires precise dose individualization, yet published machine-learning studies report accuracy that is inversely correlated with evaluation rigor: the field’s best-reported mean absolute er...
The increasing use of AI in different societal contexts intensified the debate on risks, ethical problems and bias. Accordingly, promising research activities focus on debiasing to strengthen fairness, accountability and transparency in machine learn...
The discovery of energy materials increasingly couples high-throughput computation with machine learning, but the data and models behind a given study are rarely left in a state that lets others, or the original authors months later, re-run or extend...
Background: Emergency triage systems using machine learning traditionally rely on structured tabular data (vital signs), creating a “contextual blind spot” that ignores diagnostic information embedded in unstructured clinical narratives....
Cognitive diagnosis serves as a key component in personalized intelligent education, designed to accurately evaluate students’ knowledge states by analyzing their historical response data. It offers fundamental support for various educational a...
In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automate...
The EU AI Act makes fairness metrics for high-risk AI systems’ compliance evidence, turning their trustworthiness into a safety and accountability concern. Fairness audits assume that test data reflects the properties of real-world conditions,...
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