Machine learning algorithms are increasingly used to make or support important decisions about people's lives. This has led to interest in the problem of fair classification, which involves learning to make decisions that are non-discriminatory with ...

In this abstract, I intend to outline a number of concurrent multidisciplinary research programmes in which I am engaged. Firstly, I will briefly outline my current PhD research in quantum machine learning and its connections to philosophical and ...

Machine learning -- the part of artificial intelligence aimed at eliciting knowledge from data and automated decision making without explicit instructions -- is making great strides, with new algorithms being invented every day. These algorithms find ...

Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind predictions and queries is important when assessing how the learner works and, in turn, trust. Consequently, ...

Equalized odds -- where the true positive rates and false positive rates are equal across groups (e.g. racial groups) -- is a common quantitative measure of fairness. Equalized outcomes -- where the difference in predicted outcomes between groups is ...

Prediction systems are successfully deployed in applications ranging from disease diagnosis, to predicting credit worthiness, to image recognition. Even when the overall accuracy is high, these systems may exhibit systematic biases that harm specific ...

In order for artificial agents to coordinate effectively with people, they must act consistently with existing conventions (e.g. how to navigate in traffic, which language to speak, or how to coordinate with teammates). A group's conventions can be ...

Tn this paper I set out a new Kantian approach to resolving conflicts and dilemmas of obligation for semi-autonomous machine agents such as self-driving cars. First, I argue that efforts to build explicitly moral machine agents should focus on what Kant ...

Inferring a person's goal from their behavior is an important problem in applications of AI (e.g. automated assistants, recommender systems). The workhorse model for this task is the rational actor model - this amounts to assuming that people have ...

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the challenge of how the moral values and views of all stakeholders can be integrated and reflected in ...

While various traditions under the 'virtue ethics' umbrella have been studied extensively and advocated by ethicists, it has not been clear that there exists a version of virtue ethics rigorous enough to be a target for machine ethics (which we take to ...

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information as part of a statistical model, and any commitment to protect these ...

In contexts such as college admissions, hiring, and image search, decision-makers often aspire to formulate selection criteria that yield both high-quality and diverse results. However, simultaneously optimizing for quality and diversity can be ...

Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent. For instance, judges in at least nine states make use of algorithmic tools meant to determine "...

Over the past few years, specialised online and offline press blossomed with articles about art made "with" Artificial Intelligence (AI) but the narrative is rapidly changing. In fact, in October 2018, the auction house Christie's sold an art piece ...