Our Work
This paper identifies a new role for mathematics in scientific practice. Atoosa calls this the "bridging'' role of mathematics, according to which mathematics acts as a connecting scheme in our explanatory reasoning about why and how two different descriptions of an empirical phenomenon relate to each other.
Read MoreWe propose an end-to-end model which generates captions for images embedded in news articles. News images present two key challenges: they rely on real-world knowledge, especially about named entities, and they typically have linguistically rich captions that include uncommon words. We address both.
Read MoreThis study represents the first systematic, pre-registered attempt to establish whether and to what extent the YouTube recommender system tends to promote radical content. Our results are consistent with the radicalization hypothesis. We discuss our findings, as well as directions for future research and recommendations for users, industry, and policy-makers.
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The adoption of emotion detection technology is rapidly expanding. Facebook in particular has received significant media attention in this regard. But how does the continued development and deployment of this technology in an online setting fit within the current EU regulatory framework?
Read MoreIn this conference paper, Dr Will Bateman presented a technically-embedded analysis of doctrinal legal issue which arise in the use of artificial intelligence (AI) by regulators, government administrators and other legal actors. The paper was delivered to the collected Justices of the Supreme Court of New South Wales, with special guest Justices from the High Court of Australia and the Supreme Court of the United Kingdom.
Read MoreThe paper extends a deep learning model known as graph neural networks and uses it to learn generalised heuristics. We show that these heuristics generalise to problems with different goals, larger problems, and even problems from different domains than those we trained on. This is the first paper that successfully learns domain-independent heuristics.
Read MoreThis chapter explores the alignment of the EU data protection and consumer protection policy agendas through a discussion of the reference to the Unfair Contract Terms Directive in Recital 42 of the General Data Protection Regulation.
Read MoreThis paper considers strategies for making decisions in the face of severe uncertainty, when one's beliefs are best represented by a set of probability functions over the possible states of the world (as opposed to a single precise probability function). The question is whether one can employ a decision strategy that does not have the disadvantage of making one vulnerable to sure loss in sequential-decision scenarios.
Read MoreASNETs is a neural network architecture that can learn how to solve large planning and sequential decision making problems in a domain, from examples of plans or policies for small problems in that domain.
Read MoreIn this paper, published in Artificial Intelligence, Alban Grastien and co-author address the problem of conformant planning, which consists in finding a sequence of actions in a well-specified environment that achieves a specified goal despite uncertainty on the initial configuration and without using observations.
Read MoreThis paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects. It calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects.
Read MoreWhat problem can we have with a sequences of actions if each individual act or omission is itself permissible? We use analogies from the rational choice literature to answer this puzzle, appealing to the existence of global moral norms that apply to sequences of acts.
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