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PhD International Awards in The Area of Bias

Outstanding international students are invited to apply for a PhD scholarship in artificial intelligence in Italy. The funds for this fellowship are available for three years. Applications should be submitted as soon as possible.

The Department of Computer Science at the University of Pisa (UNIPI) has two open PhD positions in the area of Bias in Artificial Intelligence. Within the department, the research group KDD Lab (Knowledge Discovery and Data Mining) is actively involved in the area of Ethical Artificial Intelligence with pioneering contributions on discrimination discovery, fairness in ML, explainable AI, privacy, and segregation discovery.

The 2 PhD positions are part of NoBIAS - Artificial Intelligence without Bias, a project funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie – Innovative Training Networks (ITN), Grant Agreement No. 860630. NoBIAS aims at developing 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. NoBIAS will train a cohort of 15 ESRs (Early-Stage Researchers) to address problems with bias through multi-disciplinary training and research in computer science, machine learning, artificial intelligence, law and social science.

ESRs of the NoBIAS project will acquire practical expertise in a variety of sectors from healthcare, 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. Each ESR will work in an individual research project in a different host institution and will participate in academic and inter-sectoral secondments at the premises of other NoBIAS members. The positions are offered full-time for three years (36 months). As a successful candidate, you will:

Motivation & Objectives: The ability to learn causality is a significant component of human-level intelligence which is hard to replicate in AI. This PhD thesis addresses the discovery and understanding of causal influences among variables. Approaches will be designed for learning such influences from the data or, for a given domain, both from data and domain knowledge. They will advance state-of-the-art in data sanitization, fair decision making, and explainability of AI. Secondments will provide grounds on the representational models of expert knowledge and for experimentation/validation in the application scenarios of insurance and credit.

Motivation & Objectives: The rise of sophisticated ML has brought accurate but obscure decision systems, thus undermining transparency, trust, and the adoption of AI in socially sensitive and safety-critical contexts. This PhD thesis will investigate declarative approaches, based on formal logic rules, for explaining ML black boxes decisions, including operators for composing and abstracting rules into a higher level of understanding. Secondments will provide grounds on the legal acquis and validation in challenging industrial application scenarios.

Expected results: Theory and methodology of explanation, i.e., a comprehensive logical/statistical framework of data-driven discovery of explanatory rules providing understandable, solid and succinct explanations of salient properties of black boxes.
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