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How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are ethical, fair, and robust? While fairness in machine learning (ML) has gained traction in recent years, it remains unexplored, or sometimes an afterthought, in the context of ...

As video games have moved to the mainstream of entertainment and popular culture, they also have given rise to new media fears. These span concerns for player welfare such as gaming addiction, negative effects of ‘screen time,’ gambling-like mechanics, ...

Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we ...

Various metrics and interventions have been developed to identify and mitigate unfair outputs of machine learning systems. While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning ...

Automatic speech recognition (ASR) researchers are working to address the differing transcription performance of ASR by accent or dialect. However, research often has a limited view of accent in ways that reproduce discrimination and limit the scope of ...

This position paper delves into the complex intersection of Artificial Intelligence (AI) deployment, human rights implications, and discriminatory practices in recruitment within Norway. In response to the global discourse on AI regulatory ...

In the last decades, there has been a deceleration in the rates of poverty reduction, suggesting that traditional redistributive approaches to poverty mitigation could be losing effectiveness, and alternative insights to advance the number one UN ...

Information systems (IS) scholars are gradually conducting research on socio-technical issues in marginalized contexts to uncover inequities and injustices and empower them through technology. In support of the evolving IS research landscape, a dedicated ...

In the first part of this tutorial we define responsible AI and we discuss the problems embedded in terms like ethical or trustworthy AI. In the second part, to set the stage, we cover irresponsible AI: discrimination (e.g., the impact of human biases); ...

We find the Small Business Administration’s disaster-relief home loan program denies significantly more loans in areas with larger shares of minorities, subprime borrowers, and higher income inequality. We find that risk-insensitive loan pricing, a ...

To make models more understandable and correctable, I propose that the PROMISE community pivots to the problem of model review. Over the years, there have been many reports that very simple mod- els can perform exceptionally well. Yet, where are the ...

Women are underrepresented in business, academic, and political decision-making bodies across the world. To investigate the causal effect of gender representation on multilateral negotiations, we experimentally manipulate the composition of triads in a ...

This paper examines whether perceptions of discrimination affect the economic expectations of U.S. households. We focus on two forms of expectations that play a central role in economic and financial decisions of households: labor income and inflation. ...

In this position paper, we introduce a new epistemic lens for analyzing algorithmic harm. We argue that the epistemic lens we propose herein has two key contributions to help reframe and address some of the assumptions underlying inquiries into ...

In recent years, many perceptual authentication hashing schemes have been proposed, especially for image content authentication. However, most of the schemes directly use the dataset of image processing during model training and evaluation, which is ...

Gender segregation of occupations and entire industries is widespread. The segregation could be the result of perceived job-specific productivity differences between men and women. It could also result from the belief that homogeneous teams perform better ...

Various studies have shown that convicted offenders often face difficulties in finding employment. These studies, however, only examined traditional types of crime and little is known about the job opportunities of convicted cybercrime offenders. ...

Supervised learning systems are trained using historical data and, if the data was tainted by discrimination, they may unintentionally learn to discriminate against protected groups. We propose that fair learning methods, despite training on potentially ...

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ...

The “Blackbox AI” installation, developed as part of the EthicAI = LABS project, seeks to raise awareness about the social impact and ethical dimension of artificial intelligence (AI). This interdisciplinary installation explores various domains ...