Postdoctoral: AI Regulatory Sandboxes
This position is for the project “AI for a green Brussels mobility and urban environments”.
Mobility, climate and biodiversity are and will remain important problems that need to be addressed, especially in cities like Brussels. Solutions should be citizen-friendly and future-proof, requiring them to be green and sustainable. Intelligent methods based on innovative AI technologies that exploit the collective knowledge as well as the wisdom of its stake-holders can provide important keys to achieve such goals.
While pursuing such AI developments, one also needs to consider the risks associated with the construction and deployment of these tools. Any societal issue related to inequality or bias needs to be caught before the systems are actually used in the public sphere. Current regulatory initiatives are arguing the use of regulatory sandboxes, claiming that they can ensure compliance to both ethical and legal requirements of the novel AI-based procurements that could occur in the public sector and beyond.
Regulatory sandboxes aim to test new technologies transparently and to contribute to evidence-based lawmaking. This provides both public and private actors to assess their services, products and procurement processes from the perspectives of (new) regulatory regimen, and at the same time, to regulators to identify possible challenges to new regulation, such as that emerging around AI and autonomous systems. The goal here is to promote the development of innovative artificial intelligence solutions that are both ethical and responsible. The sandbox helps individual organizations ensure compliance with relevant regulations and the development of solutions that take human rights and principles into account. Despite the wide interest in regulatory sandboxes applied to AI, there are so far hardly any examples of how these are to be designed, implemented, governed and used. This project aims at developing a prototype for a regulatory sandbox focusing in particular on the case of public procurement. This research will be performed in collaboration with the knowledge center for innovative procurement at Innoviris and the responsible AI research group at Umea Universitet.
This postdoctoral researcher should have a PhD in Computer Science, Artificial Intelligence or related discipline. The candidate should have expertise in multi-agent modeling and learning either from a logic or game theoretical perspective. The applicant should have good organizational skills, a taste for interdisciplinary research, excellent scientific writing and presenting skills and be able to work independently.
September – October 2022 (Project is for 2 years)
This postdoc call is launched by the VUB Artificial Intelligence (AI) lab and the ULB Machine Learning group (MLG) aims to address both the creation of novel intelligent technology and the study of regulatory mechanisms via well-established modeling approaches. Concretely two domains are targeted: on the one hand, novel AI developments for organizing mobility in Brussels and, on the other hand, the creation of agent-based models to study the ins and outs of regulatory sandboxes in the context of innovative public procurement. Both projects are organized in collaboration with Brussels public stake-holders (e.g. STIB/MIVB, Brussels Mobility, CIRB, Innoviris), and citizens.
This work will be led by the VUB AI lab and the ULB MLG within the context of FARI – AI for the Common Good institute. FARI, https://fari.brussels. It is a joint initiative of Université Libre de Bruxelles and the Vrije Universiteit Brussel. It gathers 10 research labs and aims at bringing together an extended community of 300 researchers from different disciplines (from AI, Law, Robotics, Social Sciences, Economics). As a researcher, you will be stimulated in this context to collaborate intensively with researchers from other disciplines in order to achieve your research goals on the objectives of FARI. It received the support of the NextGenerationEU and the Brussels Capital Region (Innoviris & CIRB_CIBG).
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