Special issue "AI Bias in the Media and Beyond" - (:unkn) unknown (My University, 2026)
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications". Artificial Intelligence (AI) is becoming increasingly embedded in various aspects of modern life, significantly influencing sectors such as healthcare, journalism, and more. Despite its transformative potential, the deployment of AI systems has exposed considerable concerns regarding bias, which can result in unfair, discriminatory, or erroneous outcomes for individuals and organizations. The intersection of bias and artificial intelligence (AI) is a critical area of research and development, focusing on the ways in which biases—whether societal, cultural, or technical—can be embedded into AI systems and the impacts thereof. This Special Issue aims to explore the various dimensions of bias in AI, from data collection and algorithm design to deployment and societal implications. These biases undermine the reliability, transparency, and ethical foundations of AI technologies. Addressing AI bias is imperative to ensuring that AI systems are fair, inclusive, and beneficial for all segments of society. This Special Issue on "AI Bias" aims to explore the multifaceted nature of bias in AI tools and applications, investigating its sources, manifestations, impacts, and potential solutions within but not limited to the media. We seek to compile a diverse collection of original research articles, reviews, and case studies that delve into various dimensions of AI bias from technical, ethical, and societal perspectives. We welcome submissions from diverse fields, including digital media, journalism, computer science, ethics, political science, sociology, and law, to foster a multidisciplinary dialogue on this crucial topic. Topics of Interest: Sources of Bias in AI: Data Bias: Explore how imbalances and representativeness issues in training datasets contribute to biased AI outcomes. Algorithmic Bias: Investigate biases introduced by AI models and algorithms during development and deployment phases. Human Bias: Examine how human prejudices and biases influence AI system design, implementation, and application. Different forms of Bias: Gender Bias: Assess how AI tools can perpetuate or exacerbate gender disparities. Racial and Ethnic Bias: Study the ways in which AI systems may reinforce racial and ethnic inequalities. Bias in Multimodal Systems: Analyze biases in systems combining text, image, and audio data. Tools and applications to overcome this bias. Societal impact of AI Bias: Social and Ethical Implications: Investigate the broader societal and ethical consequences of AI bias. Economic Consequences: Assess the economic impacts of biased AI systems, particularly on marginalized communities. Sector-Specific Case Studies: Provide real-world examples of biased AI outcomes in sectors like criminal justice, hiring processes, lending decisions, healthcare, and more. Media sector Bias: Metrics and Benchmarks: Present methodologies and frameworks for detecting and measuring bias in using AI tools in journalism Tools for Assessing AI Fairness: Introduce tools and technologies designed to evaluate and ensure the fairness of AI systems when used in journalism (newsrooms). Case Studies on Bias Detection: Share practical experiences and lessons (visual journalism) learned from bias detection in journalism Regulatory frameworks for AI bias: Debiasing Techniques: Discuss techniques and strategies for debiasing datasets and algorithms. Fair AI System Design: Highlight best practices for designing AI systems that are fair and unbiased. Transparency and Explainability: Explore the role of transparency and explainability in reducing bias and enhancing trust in AI systems. Bias in Educational AI Systems: Data Bias in Educational Tools: Examine how imbalances in educational datasets (e.g., standardized test scores, student demographics) contribute to biased AI outcomes in educational applications. Algorithmic Bias in Learning Platforms: Investigate biases introduced by AI algorithms used in personalized learning platforms, assessment tools, and student performance prediction models. Human Bias in Educational AI Design: Analyze how educators' and developers' biases can influence the design, implementation, and application of AI in educational settings. Racial and Ethnic Bias in Educational AI: Study the ways in which AI systems used in education may reinforce racial and ethnic inequalities in access, engagement, and achievement. Metrics and Benchmarks for Educational Fairness: Present methodologies and frameworks for detecting and measuring bias in AI tools used in education. Debiasing Techniques for Educational AI: Discuss techniques and strategies for debiasing datasets and algorithms specifically in the educational context. Transparency and Explainability in Educational AI: Explore the role of transparency and explainability in reducing bias and enhancing trust in AI systems used in education.