Ethical AI and Responsible Data Science

Dear Colleagues,

This Special Issue invites manuscripts that focus on developing ethical frameworks and AI methodologies through the lens of responsible AI and data science. AI and data science are reshaping industries, governance, and societal functions at an unprecedented pace. While these technologies offer immense potential for innovation and operational efficiency, they also raise significant ethical challenges. Issues such as algorithmic bias, lack of transparency in decision making, inequitable outcomes, and privacy violations underscore the critical need for rigorous research.

As AI systems increasingly influence high-stakes decision making, there is a growing demand for evidence-based research and actionable frameworks to ensure their ethical and responsible use. Submissions should aim to operationalize responsible AI and data science to tackle complex societal issues. We encourage contributions from researchers, practitioners, and experts in AI and machine learning who are committed to advancing the responsible development and deployment of AI technologies. This Special Issue aims to provide a platform for advancing academic discourse and practical insights in these critical areas.

We invite high-quality original research articles, comprehensive reviews, and insightful case studies that address the ethical dimensions of AI and data science. Contributions may focus on theoretical frameworks, practical applications, or empirical studies on topics of interest that include, but are not limited to, the following:

• Frameworks for Ethical AI Development: Proposals for principles, policies, and guidelines to ensure AI's ethical development and deployment;
• Responsible Data Science Practices: Approaches to align data collection, analysis, and utilization with ethical standards and societal needs;
• Fairness, Accountability, and Transparency in AI Systems: Examination of technical and social methods to achieve equitable and explainable AI;
• Bias Detection and Mitigation in Machine Learning Models: Techniques to identify, quantify, and reduce bias in AI systems and datasets;
• Privacy-Preserving Data Analysis Techniques: Exploration of advanced methods for protecting user data, including federated learning and differential privacy;
• Societal and Cultural Impacts of AI-Driven Decision Making: Investigations into how AI influences societal norms, cultural practices, and individual agency;
• Ethical Challenges in AI-Driven Sectors: Focus on healthcare, education, criminal justice, and governance, highlighting sector-specific ethical concerns;
• Regulatory and Policy Implications: Analysis of existing regulations and policy recommendations to foster ethical AI deployment;
• Human-Centered AI: Discussions on designing AI systems that prioritize human values and align with societal goals;
• Intersectionality in AI Ethics: Studies addressing how AI impacts various demographic groups differently, considering factors like race, gender, and socio-economic status.

Dr. Donghee Shin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• ethical AI
• human-centered AI
• responsible data science
• fairness in AI
• accountability in machine learning
• transparency in AI systems
• bias mitigation
• privacy-preserving analytics
• AI ethics frameworks
• societal impacts of AI
• data-driven decision-making ethics