Related papers: A Systematic Mapping on Software Fairness: Focus, …
Decisions suggested by improperly designed software systems might be prone to discriminate against people based on protected characteristics, such as gender and ethnicity. Previous studies attribute such undesired behavior to flaws in…
In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data…
Machine learning (ML) algorithms are increasingly deployed to make critical decisions in socioeconomic applications such as finance, criminal justice, and autonomous driving. However, due to their data-driven and pattern-seeking nature, ML…
Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning…
Current developments in AI made it broadly significant for reducing human labor and expenses across several essential domains, including healthcare and finance. However, the application of AI in the actual world poses multiple risks and…
\textbf{Background:} Fairness and diversity are receiving growing attention in software engineering, particularly as AI and machine learning systems increasingly influence decision-making processes. While fairness is often examined at the…
Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations…
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The literature of algorithmic fairness has grown considerably over…
As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing…
Equity is crucial to the ethical implications in technology development. However, implementing equity in practice comes with complexities and nuances. In response, the research community, especially the human-computer interaction (HCI) and…
Fairness has been a critical issue that affects the adoption of deep learning models in real practice. To improve model fairness, many existing methods have been proposed and evaluated to be effective in their own contexts. However, there…
Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different…
The rapid trend of deploying artificial intelligence (AI) and machine learning (ML) systems in socially consequential domains has raised growing concerns about their trustworthiness, including potential discriminatory behaviours. Research…
Algorithmic fairness is receiving significant attention in the academic and broader literature due to the increasing use of predictive algorithms, including those based on artificial intelligence. One benefit of this trend is that algorithm…
Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies…
Context: Successfully addressing stakeholder concerns that are related to software system development and operation is crucial to achieving development goals. The importance of using a systematic approach to addressing these concerns…
Fairness in machine learning (ML) applications is an important practice for developers in research and industry. In ML applications, unfairness is triggered due to bias in the data, curation process, erroneous assumptions, and implicit bias…
Many interesting problems in the Internet industry can be framed as a two-sided marketplace problem. Examples include search applications and recommender systems showing people, jobs, movies, products, restaurants, etc. Incorporating…
Software architecture related issues are important for robotic systems. Architecture centric development and evolution of software for robotic systems has been attracting researchers attention for more than two decades. The objective of…
As machine learning (ML) systems become central to critical decision-making, concerns over fairness and potential biases have increased. To address this, the software engineering (SE) field has introduced bias mitigation techniques aimed at…