Driving data futures: Technology and government – the good, the bad, and the ugly

Learn more Register here Add to Calendar 05/02/2019 05:15 PM 05/02/2019 07:00 PM Europe/London Driving data futures: Technology and government – the good, the bad, and the ugly Location of the event
Thursday 02 May 2019
Time: 17:15 - 19:00

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Event series

Driving data futures


In the lecture series Driving data futures, the public policy programme of The Alan Turing Institute invites audiences to learn and critically engage with new research at the intersection of new technologies, public policy, and ethics.

In this first event of the series, we will focus on the different ways technology can be used in government and discuss what is “the good, the bad, and the ugly”.

About the event

Data science, AI, and statistical modelling have far-reaching and diverse capabilities. They allow for the processing and ordering of vast amounts of structured and unstructured data and find application in scoring, classification, targeting, profiling, even prediction. Such technologies promise a myriad of benefits and public authorities are beginning to take notice. Faced with increasing budget cuts and rising service demands, public bodies are looking to algorithms to help them deliver what they see as a more objective, efficient and accessible services. But when is technology actually used for the common good and how can we ensure that this is always the case?

Two speakers will come present the results of their research on the topic.

Dr Omar Guerrero will give a speech on Policy Priority Inference for Sustainable Development

Guidelines to support international development of developing countries are many. Today, the best example of these guidelines is the Sustainable Development Goals (SDGs) – a set of 17 general goals monitored through 232 development indicators. Achieving these goals is, however, a complex task. There are interdependencies between development goals as well as inefficiencies and uncertainties in policymaking processes (e.g. corruption). A clearer understanding of development is needed to evaluate SDGs, to align environmental policies, to coordinate anti-poverty policies, and to better understand the synergies and trade-offs between development goals.

The research project Policy Priority Inference (PPI) has been established to overcome these challenges. By specifying the policymaking process through a political economy game on a network of spill-over effects, PPI accounts for the network of interdependencies between policy issues (such as the sustainable development goals), as well as well-known political economy problems arising from budget assignment (e.g. corruption). At the core of the project is an agent-computing model that simulates – from bottom up – the observed dynamics of development indicators. This tool allows us to circumvent several limitations of traditional statistical methods (e.g. losing country-specificity from cross-national estimates).

In this talk, Omar will introduce the PPI methodology and demonstrate some of the various applications where it sheds new light on economic policies for development. This is for example the case in estimating policy resilience, studying ex-ante policy evaluation, quantifying policy coherence, and assessing the effectiveness of governance reforms in the fight against corruption. In addition, we will discuss the direction that PPI is taking towards the SDG 2030 agenda and ongoing collaborations with the United Nations Development Programme (UNDP). The talk concludes that agent modelling and data science can find positive applications for international development and data-driven policy-making.


Following this, Dr Lina Dencik will speak about Social Justice in an Age of Datafication

Turning away from a ‘big picture’ view, Lina Dencik will speak about the use of data and algorithmic processes for decision-making affecting individuals and social life. Digitally monitoring, tracking, profiling and predicting human behaviour and social activities is what underpins the information order now frequently described as surveillance capitalism. Increasingly, it is also what helps determine decisions that are central to our ability to participate in society, such as welfare, education, crime, work, and if we can cross borders. A recent report from the Data Justice Lab at Cardiff University showcases that such technologies are already being used by local authorities across the UK. How should we understand what is at stake with such developments?

Often, we are dealt a simple binary that suggests that the issue is one of increased (state-)security and efficiency on the one hand and concerns with privacy and protection of personal data on the other. However, it is becoming increasingly clear that we need a broader framework for understanding these developments. This is one that can account for the disparities in how different people might be implicated and that recognises that the turn to data-driven systems is not merely technical, but a distinctly political development.

In this presentation Lina Dencik will advance a research framework for studying datafication that is rooted in a broader concern for social justice. Such a framework, referred to here as ‘data justice’, pays particular attention to the ways in which data processes are uneven, can and do discriminate, create new social stratifications of ‘have’ and ‘have nots’, and advance a logic of prediction and pre-emption that fundamentally transforms political process. In outlining such a framework, Line advances an engagement with data politics, as the performative power of or in data, that considers how the implementation of data-driven technologies in different contexts relate to wider interests, power relations, and agendas.



17:15 – Doors open

17:30 – 17:35 – Introduction

17:35 – 18:05 – Omar Guerrero – Policy Priority Inference for Sustainable Development

18:05 – 18:35 – Lina Dencik – Social Justice in an Age of Datafication

18:35 – 19:00 – Q&A