Related papers: Data Justice in Digital Social Welfare: A Study of…
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,…
Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against…
Humanitarian organizations provide aid to people in need. To use their limited budget efficiently, their distribution processes must ensure that legitimate recipients cannot receive more aid than they are entitled to. Thus, it is essential…
The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data…
The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a case study, we examine how these issues persist. Rather than…
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or…
Digital discrimination is a form of discrimination whereby users are automatically treated unfairly, unethically or just differently based on their personal data by a machine learning (ML) system. Examples of digital discrimination include…
Most proposed algorithmic fairness techniques require access to data on a "sensitive attribute" or "protected category" (such as race, ethnicity, gender, or sexuality) in order to make performance comparisons and standardizations across…
We study how a monopolist's use of consumer data for price discrimination affects welfare. To answer this question, we develop a model of market segmentation subject to residual uncertainty. We fully characterize when data usage…
In 2020, a Dutch court passed judgment in a case about a digital welfare fraud detection system called Systeem Risico Indicatie (SyRI). The court ruled that the SyRI legislation is unlawful because it does not comply with the right to…
The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a…
Fairness in data-driven decision-making studies scenarios where individuals from certain population segments may be unfairly treated when being considered for loan or job applications, access to public resources, or other types of services.…
Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known…
Fairness in online advertising is often formalized as a distributive justice problem, aiming to ensure that impressions, opportunities, or outcomes are allocated comparably across protected groups. Yet online advertising can still produce…
The idea of "data justice" is of recent academic vintage. It has arisen over the past decade in Anglo-European research institutions as an attempt to bring together a critique of the power dynamics that underlie accelerating trends of…
Social interaction across multiple online platforms is a challenge for gender and sexual minorities (GSM) due to the stigmatization they face, which increases the complexity of their self-presentation decisions. These online interactions…
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit…
The demographic disparity of biometric systems has led to serious concerns regarding their societal impact as well as applicability of such systems in private and public domains. A quantitative evaluation of demographic fairness is an…
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from…
Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g. access control) and non-cooperative (e.g. surveillance and forensics)…