Department of Statistics

A brief introduction to various legal and procedural ethical concepts and their applications within and beyond academia. It's all very well to talk about truth, beauty and justice for academic research ethics.  But how do you do these things at a practical level?  If you have a big idea, or stumble across something with important implications, what do you do with it?  How do you make sure you've got appropriate safeguards without drowning in bureaucracy?  

The practicalities of academic research ethics - how to get things done

Victims of Algorithmic Violence: An Introduction to AI Ethics and Human-AI Interaction

A brief introduction to various legal and procedural ethical concepts and their applications within and beyond academia. It's all very well to talk about truth,

A lecture exploring alternatives to using labeled training data.  Labeled training data is often scarce, unavailable, or can be very costly to obtain. To circumvent this problem, there is a growing interest in developing methods that can exploit sources of information other than labeled data, such as weak-supervision and zero-shot learning.  While these techniques obtained impressive accuracy in practice, both for vision and language domains, they come with no theoretical characterization of their accuracy. In a sequence of recent works, we develop a rigorous mathematical framework for constructing and analyzing algorithms that combine multiple sources of related data to solve a new learning task. Our learning algorithms provably converge to models that have minimum empirical risk with respect to an adversarial choice over feasible labelings for a set of unlabeled data, where the feasibility of a labeling is computed through constraints defined by estimated statistics of the sources. Notably, these methods do not require the related sources to have the same labeling space as the multiclass classification task. We demonstrate the effectiveness of our approach with experimentations on various image classification tasks.

A Theory of Weak-Supervision and Zero-Shot Learning

A high-level overview of key areas of AI ethics and not-ethics, exploring the challenges of algorithmic decision-making, kinds of bias, and interpretability, linking these issues to problems of human-system interaction. Much attention is now being focused on AI Ethics and Safety, with the EU AI Act and other emerging legislation being proposed to identify and curb "AI risks" worldwide. Are such ethical concerns unique to AI systems - and not just digital systems in general? 

