The Data Science Institute at Lancaster University

Data Science Institute

We aim to set the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. Established in 2015, the Data Science Institute (DSI) has over 300 members and has raised nearly £35 million in research grants.

Research Themes

Latest Events

DSI Inequalities Retreat - The Midland Hotel, Morecambe

On the 19th and 20th May the Society theme had a fantastic two-day retreat on the topic of Inequalities, marking the kick-off of a new strategic focus within the DSI. Academics from across the university came together for lively interdisciplinary discussion exploring a wide range of inequalities and discovering connections between fields of health, sociology, politics and policy, economics, environment, computing, and design. We look forward to building on these conversations with further opportunities for members to meet up and collaborate in the months to come.

Day 1 of the retreat offered 4 clusters of talks, interspersed with time for food, quality conversations and some relaxation.


Local inequalities: understanding and enriching the region

  • Jo Knight (Eden North)
  • Emma Halliday (Health Research)
  • Michelle Collins (Health Research)
  • Chris Boyko (Lancaster Institute for the Contemporary Arts)


International inequalities: tackling global challenges

  • Michaela Benson (Sociology)
  • Sally Cawood (Lancaster Environment Centre)
  • Jasmine Fledderjohann (Sociology) recording


Making sense of inequalities: the value of large-scale datasets

  • Ian Walker (Lancaster University Management School)
  • Katie Hunter (Law School)
  • Steffi Doebler (Sociology)
  • Heather Brown (Health Research)


Inequalities: the politics of infrastructure and environment

  • Gordon Walker (Lancaster Environment Centre)
  • Bran Knowles (School of Computing and Communications)
  • Matthew Johnson (Politics and Policy)

Day 2 provided information on the current funding landscape, the University’s new Secure Data Science Infrastructure, and provided unstructured time for your own work, thinking or collaborative conversations.

Talks were given by the following people -

Inequalities research: the funding landscape (Odette Dewhurst, Senior Research Development Manager, RES)

NIHR North West Research Support and Development Team, Julie Mugarza and James Connolly plus Q&A

Lancaster’s Secure Data Science Infrastructure: Karen Broadhurst & Geraint Harries (ISS) Audience Q&A

The Midland Hotel

Distinguished Speaker - Dr Brent Mittelstadt, gave a talk in May to DSI on: “Talk on bias, fairness, and non-discrimination law in artificial intelligence”

Dr Brent Mittelstadt from the Oxford Internet Institute gave a talk to DSI

Abstract: Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognising this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias metrics. Often these metrics address technical bias but ignore the underlying causes of inequality and take for granted the scope, significance, and ethical acceptability of existing inequalities.

A recording of this talk is now on YouTube.

In this talk I will introduce the concept of “bias preservation” as a means to assess the compatibility of fairness metrics used in machine learning against the notions of formal and substantive equality. The fundamental aim of EU non-discrimination law is not only to prevent ongoing discrimination, but also to change society, policies, and practices to ‘level the playing field’ and achieve substantive rather than merely formal equality. Based on this, I will introduce a novel classification scheme for fairness metrics in machine learning based on how they handle pre-existing bias and thus align with the aims of substantive equality. Specifically, I will distinguish between ‘bias preserving’ and ‘bias transforming’ fairness metrics. This classification system is intended to bridge the gap between notions of equality, non-discrimination law, and decisions around how to measure fairness and bias machine learning. Bias transforming metrics are essential to achieve substantive equality in practice. I will conclude by introducing a bias preserving metric ‘Conditional Demographic Disparity’ which aims to re-frame the debate around AI fairness, shifting it away from which is the right fairness metric to choose, and towards identifying ethically, legally, socially, or politically preferable conditioning variables according to the requirements of specific use cases.

Research Themes

Data Science at Lancaster was founded in 2015 on Lancaster’s historic research strengths in Computer Science, Statistics and Operational Research. The environment is further enriched by a broad community of data-driven researchers in a variety of other disciplines including the environmental sciences, health and medicine, sociology and the creative arts.

  • Foundations

    Foundations research sits at the interface of methods and application: with an aim to develop novel methodology inspired by the real-world challenge. These could be studies about the transportation of people, goods & services, energy consumption and the impact of changes to global weather patterns.

  • Health

    The Health theme has a wide scope. Current areas of strength include spatial and spatiotemporal methods in global public health, design and analysis of clinical trials, epidemic forecasting and demographic modelling, health informatics and genetics.

  • Society

    Data Science has brought new approaches to understanding long-standing social problems concerning energy use, climate change, crime, migration, the knowledge economy, ecologies of media, design and communication in everyday life, or the distribution of wealth in financialised economies.

  • Environment

    The focus of the environment theme has been to seek methodological innovations that can transform our understanding and management of the natural environment. Data Science will help us understand how the environment has evolved to its current state and how it might change in the future.

Professor Christina Pagel

Professor Christina Pagel gave a talk to DSI on 'What Independent SAGE has taught me about the current biggest issues in light of COVID-19 and where data science can help'. Listen here to her insights and observations about the ongoing pandemic.

DSI Society - Inequalities

A recording is now available of the launch of the book by Amy Clair and our very own Jasmine Fledderjohann and Bran Knowles entitled, "A Watershed Moment for Social Policy and Human Rights?: Where Next for the UK Post-Covid". The event included an overview of the key concepts and themes in the book; invited talks from Aaron Reeves (University of Oxford), Kayleigh Garthwaite (University of Birmingham), and Daniel Greene (University of Maryland).

DSI Video

In 2019 we recorded a short film to document the research themes and activities and to explain the Institute structure of Data Science at Lancaster.

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