Our Work
This chapter, by Peggy Valcke, Damian Clifford, and Viltė Kristina Steponėnaitė, will appear in Constitutional Challenges in the Algorithmic Society, Giovanni De Gregorio et al. (eds) (Forthcoming CUP). It discusses legal-ethical challenges posed by the emergence of emotional artificial intelligence (AI) and its manipulative capabilities.
Read MoreThis article discusses personal data, personal information, internet of things, and consumer law.
Read MoreThere has long been debate within the scholarly literature around the role and the limits of consent in promoting welfare enhancing outcomes and the need for consent-based gate-keeping mechanisms to be supplemented by other protections. Moves to bolster consent within the field of consumer privacy, and indeed, the criticisms of relying on it, should be couched within this broader literature.
Read MoreThis paper is a collaboration between HMI, IAG and Gradient, and reflects our broader concern that new methods that use machine learning to influence risk predictions to determine insurance premiums won't be able to distinguish between risks the costs of which people should bear themselves, and those that should be redistributed across the broader population, and might also involve using data points that it is intrinsically wrong to use for this purpose.
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 a paper published in Plos One, Colin Klein and co-authors shed light on the online world of conspiracy theorists, by studying a large set of user comments. Their key findings were that people who eventually engage with conspiracy forums differ from those who don’t in both where and what they post. The patterns of difference suggest they actively seek out sympathetic communities, rather than passively stumbling into problematic beliefs.
Read MoreColin Klein was interviewed by ABC Drive and 2CC Canberra about conspiracy theories surrounding COVID-19 and the role of online information platforms such as twitter in propagating misinformation.
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 MoreEpidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the two model classes via three new mathematical components.
Read MoreLittle is known about how human attention is allocated over the large-scale networks used by most video hosting sites and about the impacts of the recommender systems they use. In this paper, we propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines.
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