PhD project title

Fair AI for the Public Sector: Advancing the Technological Frontier

Outline description, including interdisciplinary, intersectoral and international dimensions 

Machine learning (ML), the discipline that forms the contemporary mainstream of AI research, targets to develop algorithms designed to leverage patterns within datasets to address specific tasks and challenges. By way of example, a simple association (rule) mining algorithm might identify insights from shopping patterns to inform decisions on product placement in stores, or to send marketing offers to customers. Left to be guided with objectives such as maximization of (predictive) accuracy or its economic manifestations such as advertising revenue, conventional ML algorithms can leverage the existence of benign patterns of user behaviour present in the dataset, such as frequent co-buying of milk and bread.

However, the data often contains other kinds of patterns as well, those which would be considered less benign and instead more reflective of societal biases that legitimate buying choices. ML algorithms have no way to differentiate between desirable and undesirable patterns, being only guided by maximization of accuracy. Both kinds of patterns would be leveraged in the insights they generate given that they are both likely useful. Thus, ignoring undesirable patterns to make fairer decisions incurs a cost in terms of conventional measures such as accuracy and advertising revenue.

These concerns are supremely important to the issue of ML algorithms within the public sector, where fairness is an internationally-understood value in citizen-state interactions and the elimination of bias a legal and organisational objective of public sector organisations.

This project focuses on the development of fair ML algorithms for unsupervised learning tasks such as clustering, retrieval, representation learning and anomaly detection. In practice, this project will involve developing an understanding of the principles of justice and fairness from political philosophy and translating them into mathematical formulations upon which scenario-specific bespoke fair ML algorithms would be built. The project will have considerable potential for the increasing number of public sector organisations that rely on ML to conduct transaction and other processes within government.

Key words/descriptors

Fairness, Public Sector, Machine-Learning, Algorithms

Fit to CITI-GENS theme(s)

• Information Technology

This project will fall under the Information Technology theme of CITI-GENS.

Supervisor Information

First Supervisor:    Deepak Padmanabhan                                                          School: EEECS

Second Supervisor:    Muiris MacCarthaigh                                                        School: HAPP

Third Supervisor:    Sameep Mehta                                                                      Company: IBM Research

Name of non-HEI partner(s)

IBM Research (India)

Contribution of non-HEI partner(s) to the project:

IBM Research will host the student for an internship in 2021 (already agreed), and any subsequent internships during the course of the project. During the internship, IBM Research will provide coaching and mentoring to the student, and the student will be considered a full part of a research group on machine learning fairness within IBM Research – India. This will give the student valuable exposure to industry life as well as practical perspectives of fair ML work oriented towards the industry. IBM Research is a world leading research organization, and its India arm, IBM Research – India, has a research group on Fair AI. They have been very active in research in fair machine learning, and have generated several publications across the past few years.

Subject Area

Computer Science