Algorithmic government: authority and accountability in automated migration governance

This inter-disciplinary project critically investigates the design and deployment of machine-learning in governing migration. Machine-learning has become a commonplace aspect of how migrants are governed. It is used in autonomous vehicles, facial recognition, border-control and humanitarian risk assessments. The design of these tools is developed from a priori data, and data-mining exercises that track existing movement. The applications of these tools combine two aspects of rule into unified (but not simple) automated processes, apparently packaged together in a single decision-making systems: on the one hand, the power of governmental rules on rights, integration and belonging and on the other, the day-to-day decision-making on who is deported, monitored, who stays where and when. As such, machine-learning tools seem to promise legal clarity, consistency, speed, and communication across different regulatory authorities in integrated systems, thus ‘managing’ apparently threatening or vulnerable population movement. Yet even in migration governance – one of the earliest applications of machine-learning in government – regulatory boundaries, legal authority and technological limits confound these promises. In practice, ‘algorithmic government’ and the automation of decision-making involve complex combinations of machine-learning code; integration of data generation, mining, use and storage; and intersecting technological systems, as well as human interpretation and action.

This project will examine the social, political and technological conditions shaping the design and use of these tools of population management. In their project proposals, applicants are encouraged to investigate particular technologies and/or their application to specific aspects of migration governance. The project will address some combination of the following questions, applied to a specific empirical case/cases: How are the parameters of specific tools, and the terms and conditions of their production established? What are the limits, and implications, of the specific form of automation encoded in particular tools? What data on mobility and risk do they use, and under what terms, rules and conditions? How are machine-learning tools experienced by, and how do they shape the actions of, decision-makers? How is the design of machine-learning algorithms integrated with governmental practices in specific decision-making contexts (e.g. in particular bordering processes like visa control; in particular places, like camps, ports, airports) and does this integration process vary? How is accountability, authority, and responsibility for decision-making distributed in such varying contexts?

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI) which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/..

The project will contribute to explanations of how AI affects the social world and it will use social scientific concepts to do so. Its empirical discussion will also contribute to computer science and engineering understandings of the ethical, political and social implications of machine-learning and/or automotive design in governmental practice in complex legislative environments.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

In addition, it is anticipated that applicants will have a background in a social science related subject, such as sociology, politics and/or geography. They should have good quantitative skills to enable them to take full advantage of the interdisciplinary training. Prior knowledge and work on computer-based decision-making would be an advantage, but is not essential. Students will receive training tailored to their background and project. This may include: programming, digital data and advanced quantitative methods (social statistics), AI ethics, AI and government.

Informal enquiries about the project should be directed to Dr Emma Carmel on email address [Email Address Removed].

Enquiries about the application process should be sent to [Email Address Removed].

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.