Prf Miriam Fernandez

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime entailing risks, such as being denied a credit, a job, a medical treatment, or specific news. Businesses might miss chances, because biases make AI-driven decisions underperform; much worse, they may contravene human rights when treating people unfairly. Bias may arise at all stages of AI-based decision making processes: (i) when data is collected, (ii) when algorithms turn data into decision making capacity, or (iii) when results of decision making are used in applications. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in the training, design and deployment of AI algorithms to ensure social good while still benefiting from the potential of AI. NoBIAS will develop novel methods for AI-based decision making without bias by taking into account ethical and legal considerations in the design of technical solutions. The core objectives of NoBIAS are to understand legal, social and technical challenges of bias in AI-decision making, to counter them by developing fairness-aware algorithms, to automatically explain AI results, and to document the overall process for data provenance and transparency. We will train a cohort of 15 ESRs (Early-Stage Researchers) to address problems with bias through multi-disciplinary training and research in computer science, data science, machine learning, law and social science. ESRs will acquire practical expertise in a variety of sectors from telecommunication, finance, marketing, media, software, and legal consultancy to broadly foster legal compliance and innovation. Technical, interdisciplinary and soft-skills will give ESRs a head start towards future leadership in industry, academia, or government.

Our project is dedicated to advancing socio-technical approaches to fairness in Higher Education within the UK, with a specific focus on addressing bias and discrimination in AI-driven learning analytics systems. As technology increasingly shapes the student experience and educational outcomes, institutions worldwide are recalibrating their data strategies for positive impacts on student success. However, the integration of fairness, both technically and socially, remains an understudied aspect of these strategies. Leveraging the successful OUAnalyse system at the Open University, our project seeks to establish a framework of best practices, risks, and compliance with UK regulations. This initiative aims to guide Higher Education institutions in the UK as they develop their digital strategies, emphasising the core principles of fairness and responsible AI. The OU Analyse Dashboard (OUA) serves as our primary use case, a Predictive Learning Analytics System supporting over 200,000 students. Through weekly alerts to tutors, the system identifies students at risk of dropping out, contributing to a noteworthy increase in retention rates, especially for ethnic minority and socioeconomically disadvantaged students. Our project is organised around five fundamental pillars: Fairness, Explainability, Transparency, Accountability, and Social Justice. In the pursuit of fairness, we will conduct a comprehensive examination of PLA's performance across diverse student cohorts, paying special attention to intersectionality. Scrutinising prediction errors, and whether they affect differently to different student groups based on sensitive characteristics, such as gender, ethnicity, disability, Index of Multiple Deprivation, and previous qualifications, we aim to uncover biases and enhance overall system fairness. Transparency is addressed by providing students with predictions and explanations, piloting a student dashboard to make predictions visible and empowering students to take proactive measures in collaboration with tutors. In terms of accountability, we will investigate tutor reactions to predictions, ensuring equitable support for all students. The project will contribute to the broader discourse on ethical AI implementation in education, aligning with the UK government's innovation-focused approach to AI regulation. Collaboration with regulatory bodies, such as The Equality & Human Rights Commission and The Information Commissioner's Office, will ensure compliance of the provided framework with equality and human rights law, data protection, and sector-specific legislation. Beyond technical considerations, our commitment to Equality, Diversity, and Inclusion (EDI) is central. We actively address societal inequalities, striving to mitigate biases in PLA systems and reducing awarding gaps, ensuring fair and equal opportunities for all students. Our dissemination efforts encompass scientific conferences, collaboration with regulatory bodies, and engagement with higher education communities, fostering knowledge sharing and responsible AI practices. The project's potential economic impact lies in improving student investment returns, contributing to a socially inclusive educational landscape with diversified job opportunities. Success metrics include compliance with government priorities, regional impacts on other universities, and economic and social impacts on student quality of life and inclusion. Through this comprehensive approach, our project aims to reshape the AI in the education landscape, fostering fairness, transparency, and inclusivity.

We live in cyber-physical-social spaces where lines between ‘online’ and ‘offline’ are increasingly blurred, but also where violence against women and girls (VAWG) thrives. It is therefore more important now than ever for interdisciplinary research and cross-sectoral dialogue to address this contemporary societal challenge. The Centre for Protecting Women online will be a vehicle for understanding and addressing challenges posed to women’s safety online through a novel, interdisciplinary and ambitious research agenda. E3 funding will support this expansion by combining cross-sectoral, collaborative outputs and interventions which inform law, policy, technology development, and practice to reduce online harms suffered by women and girls, minimise anti-social behaviours online whilst promoting pro-social behaviours and help build tech software that helps ensure accountability, credibility, and helps facilitate justice.