Our modern lives are increasingly governed by ubiquitous AI systems and an abundance of digital data. More and more products and services are providing us with better tools and recommendations for our professional, personal, and entertainment activities. With the...
The current state of the art in generative modelling is dominated by neural networks. Despite their impressive performance on many benchmark tasks, these algorithms do not provide tractable inference for common and important queries, e.g. marginalization and...
Data-driven approaches have been proven powerful in a variety of domains, from computer vision to NLP. However, in some domains – such as in attack detection in security – the arms race between attackers and defenders causes an ever-growing...
In recent years, AI has achieved tremendous success in many complex decision making tasks. However, when deploying these systems in the real world, safety concerns restrict — often severely — their adoption. One concrete example is that of automated ventricular...
AI systems often collect their input from humans. For example, parents are asked to input their preferences over primary schools before a centralised algorithm allocates children to schools. Should the AI trust the input provided by parents who may try to game the...
Collective intelligence (CI) communities are among the greatest examples of collaboration, capability, and creativity of the digital age. CI communities allow large groups of individuals to work together towards a shared goal. CI platforms such as Wikipedia,...
Background: Learning-based conversational agents can generate conversations that violate basic logical rules and common sense, which can seriously affect user experience and lead to mistrust and frustration. To create accurate, smart, and trustworthy conversational...
Motivation: Training data can be severely biased. The existing metrics of data bias are based on data balance situations conditioned on protected attributes. This is coarse-grained and does not consider the relationship among different attributes as well as different...
Agent-based models (ABMs) are an AI technique to help improve our understanding of complex real-world interactions and their “emergent behaviours”. ABMs are used to develop and test theories or to explore how interventions might change behaviour. For example, we are...
The problem of ethical decision making presents a grand challenge for modern AI research. Arguably, the main obstacle to automating ethical decisions is the lack of a formal specification of ground-truth ethical principles, which have been the subject of debate for...
Planning algorithms are used in a variety of contexts, from navigation apps to recommendation algorithms, robot vacuums, autonomous vehicles, etc.Companies using such algorithms have financial incentives to manipulate (or nudge) user behaviour so as to obtain valuable...
Reinforcement learning (RL) has become a new paradigm for solving complex decision-making problems. However, it presents numerous safety concerns in real world decision making, such as unsafe exploration, unrealistic reward function, etc. As reinforcement learning...
