Related papers: Dimensions of Diversity in Human Perceptions of Al…

Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial…

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision…

Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by…

This study explores perceptions of fairness in algorithmic decision-making among users in Bangladesh through a comprehensive mixed-methods approach. By integrating quantitative survey data with qualitative interview insights, we examine how…

Algorithmic processes are increasingly employed to perform managerial decision making, especially after the tremendous success in Artificial Intelligence (AI). This paradigm shift is occurring because these sophisticated AI techniques are…

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions,…

Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their…

The field of algorithmic fairness has highlighted ethical questions which may not have purely technical answers. For example, different algorithmic fairness constraints are often impossible to satisfy simultaneously, and choosing between…

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare…

As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups. Research on algorithmic bias has exploded in recent years,…

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant,…

Machine learning algorithms are increasingly used to make or support decisions in a wide range of settings. With such expansive use there is also growing concern about the fairness of such methods. Prior literature on algorithmic fairness…

Algorithms are increasingly used to guide high-stakes decisions about individuals. Consequently, substantial interest has developed around defining and measuring the ``fairness'' of these algorithms. These definitions of fair algorithms…

Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between…

The ethics of artificial intelligence (AI) systems has risen as an imminent concern across scholarly communities. This concern has propagated a great interest in algorithmic fairness. Large research agendas are now devoted to increasing…

Algorithmic systems have been used to inform consequential decisions for at least a century. Recidivism prediction dates back to the 1920s. Automated credit scoring dates began in the middle of the last century, but the last decade has…

Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns…

AI systems are increasingly used in high-stakes domains such as credit rating, where fairness concerns are critical. Existing fairness assessments are typically conducted by AI experts or regulators using predefined protected attributes and…

Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an…

Algorithmic fairness is a major concern in recent years as the influence of machine learning algorithms becomes more widespread. In this paper, we investigate the issue of algorithmic fairness from a network-centric perspective.…