Although algorithms are increasingly used to guide real-world decision-making, their potential for propagating bias remains challenging to measure. A common approach for researchers and practitioners examining algorithms for unintended discriminatory ...

AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as black-box systems, complicating ...

Voice-based systems like Amazon Alexa, Google Assistant, and Apple Siri, along with the growing popularity of OpenAI’s ChatGPT and Microsoft’s Copilot, serve diverse populations, including visually impaired and low-literacy communities. This reflects a ...

In this editorial, we define discrimination in the context of AI algorithms by focusing on understanding the biases arising throughout the lifecycle of building algorithms: input data for training, the process of algorithm development, and algorithm ...

Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, ...

In the pursuit of intersectional group fairness in machine learning models, significant attention has been directed towards fair representation learning methods. These methods aim to mitigate bias in training data by encoding data effectively while ...

As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairness. Recent work has shown that ...

ZDF is a Public Service Media (PSM) broadcaster in Germany that uses recommender systems on its streaming service platform ZDFmediathek. One of the main use cases within the ZDFmediathek is Next Video, which is currently based on a Self-Attention based ...

The research domain of recommender systems is rapidly evolving. Initially, optimization efforts focused primarily on accuracy. However, recent research has highlighted the importance of addressing bias and beyond-accuracy measures such as novelty, ...

A new criterion for assessing fairness of AI models in medical imaging is proposed. The key idea is to control for disease severity, which as a mediator, affects the presentation of disease in medical images, and hence the performance of AI ...

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 ...

In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation ...

Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions ...

With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces ...

Artificial intelligence (AI) has significantly optimized processes across various sectors, enhancing efficiency and transforming digital interactions. However, as AI becomes more integrated into daily life, concerns about its social impacts and inherent ...

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 ...

Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks ...

Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead ...

Ranking systems are instrumental in shaping user experiences by determining the relevance and order of presented items. However, current approaches, particularly those revolving around user-centric reputation scoring, raise ethical concerns associated ...

Software runs the world and should provide equal rights and opportunities to all genders. However, the gender gap exists in the software engineering workforce and many software products are still gender biased. Recently, AI systems, including modern ...