Our Work
The 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 MoreWatch the video of our webinar on COVIDSafe, 4 weeks in, here. Seth Lazar chaired a discussion with some of Australia’s leading experts in public health, privacy and cybersecurity, asking not only whether the app works and whether the risks posed to privacy are proportionate and necessary, but also about the politics of vesting authority to make major decisions about public health in unaccountable tech companies.
Read MoreIn the aggregate, advances in data analytics can now yield unexpected and highly beneficial insights into human behaviour, which the government can harness in the interests of the public. But those advances pose significant risks of harming the very people they are intended to benefit. Read more in our submission to the National Data Sharing Commission’s discussion paper on Data Sharing and Release.
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 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 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 MoreIn a joint submission, HMI identified 7 areas for further development in the Human Rights and Technology discussion paper proposed by the Australian Human Rights Commission. The main three concerned: defining ‘AI-informed decision-making’; the demand for explanations; and the absence of a formally link between design and assessment.
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 MoreIn March 2020 Seth Lazar presented a paper on machine ethics to an interdisciplinary conference at CMU. His respondent was Professor Jonathan Cohen (Princeton).
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.
Read More
