Published in Communist and Post-Communist Studies, 2005
This paper is about the bi-lateral agreements between the federal center and regions in Russia.
Recommended citation: Dusseault, David, Martin Ejnar Hansen, and Slava Jankin Mikhaylov (2005). "The significance of economy in the Russian bi-lateral treaty process." Communist and Post-Communist Studies 38: 121-130.
Published in The Legitimacy of the European Union After Enlargement, Jacques Thomassen (ed.), Oxford University Press, 2009
This paper is about support for European integration.
Recommended citation: Slava Jankin Mikhaylov and Michael Marsh (2009). "Policy Performance and Support for European Integration." In The Legitimacy of the European Union After Enlargement, Jacques Thomassen (ed.), Oxford University Press.
Published in American Journal of Political Science, 2009
This paper is about measurement error in NLP applications in political science.
Recommended citation: Benoit, Kenneth, Michael Laver and Slava Jankin Mikhaylov (2009). "Treating Words as Data with Error: Estimating Uncertainty in Text Statements of Policy Positions." American Journal of Political Science, 53 (2): 495–513.
This paper is about bias and error in human text coding.
Recommended citation: Slava Jankin Mikhaylov, Michael Laver, and Kenneth Benoit (2012). "Coder Reliability and Misclassification in the Human Coding of Party Manifestos." Political Analysis, 20(1): 78-91.
Published in British Journal of Political Science, 2012
This paper is about text units in human coding, and consequences for bias and error.
Recommended citation: Thomas Daubler, Kenneth Benoit, Slava Jankin Mikhaylov, and Michael Laver (2012). "Natural Sentences as Valid Units for Coded Political Texts." British Journal of Political Science, 42(4): 937-951.
This paper is about using manifestos for policy position estimates.
Recommended citation: Kenneth Benoit, Michael Laver, Will Lowe, and Slava Jankin Mikhaylov (2012). "How to scale coded text units without bias: A response to Gemenis." Electoral Studies, 31(3): 605-608.
Recommended citation: Jan-Emmanuel De Neve, Slava Jankin Mikhaylov, Christopher T. Dawes, Nicholas A. Christakis, James H. Fowler (2013). "Born to Lead? A Twin Design and Genetic Association Study of Leadership Role Occupancy." The Leadership Quarterly, 24(1): 45-60.
Published in Political Science Research and Methods, 2013
This paper is about leadership tools.
Recommended citation: Alexander Baturo and Slava Jankin Mikhaylov (2013). "Life of Brian Revisited: Assessing Informational and Non-Informational Leadership Tools." Political Science Research and Methods, 1(1): 139-157.
Published in Journal of Elections, Public Opinion and Parties, 2014
This paper is about electoral results in Ireland.
Recommended citation: Michael Marsh and Slava Jankin Mikhaylov (2014). "A Conservative Revolution: The electoral response to economic crisis in Ireland." Journal of Elections, Public Opinion and Parties, 24(2): 160-179.
Recommended citation: Alexander Baturo and Slava Jankin Mikhaylov (2014). "Reading The Tea Leaves: Medvedev’s Presidency Through Political Rhetoric Of Federal And Sub-National Actors." Europe-Asia Studies, 66(6): 969-992.
Published in American Political Science Review, 2016
This paper is about crowdsourcing core political science data.
Recommended citation: Kenneth Benoit, Drew Conway, Benjamin E. Lauderdale, Michael Laver, and Slava Jankin Mikhaylov (2016). "Crowd-Sourced Text Analysis: Reproducible and agile production of political data." American Political Science Review, 110(2): 278-295.
Recommended citation: Alexander Baturo, Niheer Dasandi, and Slava Jankin Mikhaylov (2017). "Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus." Research & Politics, 4(2).
Published in IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS), 2017
This paper is about Irish parliamentary debates.
Recommended citation: Alexander Herzog and Slava Jankin Mikhaylov (2017). "Database of Parliamentary Speeches in Ireland, 1919-2013." IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS), 23-25 October 2017, Xi’an, China: 29-34.
Published in IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS), 2017
This paper is about preference dynamics in UN General Debates.
Recommended citation: Stefano Gurciullo and Slava Jankin Mikhaylov (2017). "Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings." IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS), 23-25 October 2017, Xi’an, China: 74-79.
This paper is about the health effects of climate change.
Recommended citation: Nick Watts et al. (2018). "The Lancet countdown on health and climate change: from 25 years of inaction to a global transformation for public health." The Lancet, 391(10120): 581-630.
Published in Philosophical Transactions of the Royal Society A, 2018
This paper is about AI and cross-sectoral collaboration.
Recommended citation: Slava Jankin Mikhaylov, Marc Esteve, and Averill Campion (2018). "Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration." Philosophical Transactions of the Royal Society A, Volume 376, Issue 2128.
This paper is about the health effects of climate change.
Recommended citation: Nick Watts et al. (2018). "The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come." The Lancet, 392(10163): 2479-2514.
Published in Workshop on Artificial Intelligence and United Nations Sustainable Development Goals, IJCAI International Joint Conferences on Artificial Intelligence, 2019
Position paper on machine learning potential to track progress on SDG 16.
Recommended citation: Niheer Dasandi and Slava Jankin Mikhaylov (2019). "AI for SDG 16 on Peace, Justice, and Strong Institutions: Tracking Progress and Assessing Impact." AI and UN SDGs Workshop at IJCAI.
Recommended citation: N Watts et al. (2019). "The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate." The Lancet, 394(10211): 1836-1878.
This paper is about foreign policy balancing of African states between China and United States.
Recommended citation: Padraig Carmody, Niheer Dasandi, Slava Jankin Mikhaylov (2020). "Power Plays and Balancing Acts: The Paradoxical Effects of Chinese Trade on African Foreign Policy Positions." Political Studies, 68(1): 224–246.
Recommended citation: Radoslaw Kowalski, Marc Esteve, and Slava Jankin Mikhaylov. "Improving Public Services by Mining Citizen Feedback: An Application of Natural Language Processing." Public Administration, 98(4): 1011-1026.
Published in Public Policy and Administration, 2020
This paper is about AI effect on public policy research.
Recommended citation: Irina Pencheva, Marc Esteve, and Slava Jankin Mikhaylov (2020). "Big Data & AI – A Transformational Shift for Government: So, What Next for Research?" Public Policy and Administration, 35(1): 24–44.
Published in Energy Research and Social Science, 2020
Big data from smart meters can be well clustered with Gaussian Mixture Models
Recommended citation: Anastasia Ushakova and Slava Jankin Mikhaylov. "Big data to the rescue? Challenges in analysing granular household electricity consumption in the United Kingdom." Energy Research and Social Science, Volume 64, June 2020, 101428.
Published in Bulletin of the World Health Organization, 2021
Intergovernmental engagement on health impacts of climate change
Recommended citation: Niheer Dasandi, Hilary Graham, Pete Lampard, and Slava Jankin Mikhaylov. "Intergovernmental engagement on health impacts of climate change." Bulletin of the World Health Organization, 99(2): 102-111.
Engagement with health in national climate change commitments under the Paris Agreement: a global mixed-methods analysis of the nationally determined contributions.
Recommended citation: Niheer Dasandi, Hilary Graham, Pete Lampard, Slava Jankin Mikhaylov (2021). "Engagement with health in national climate change commitments under the Paris Agreement: a global mixed-methods analysis of the nationally determined contributions." Lancet Planetary Health, 5(2): 93-101.
Public sector organisations are increasingly interested in using data science capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science in the public sector relies on successfully embedding it into delivery solutions for policy implementation. This requires organisational innovation and change delivered through structural and cultural adaptation, together with capacity building. Another key factor for success is the contribution of academia and the private and third sector. This talk will discuss the opportunities that exist for using data science in delivering public services at the international and national levels.
This tutorial covers how diplomats can use data and sophisticated analytical tools (namely Natural Language Processing and advanced statistical methods) to better understand and conduct multilateral diplomacy.
Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models use unsupervised methods and hence require the additional step of attaching meaningful labels to estimated topics. This process of manual labeling is not scalable and suffers from human bias. We present a transfer learning approach to topic labeling that leverages existing knowledge-base in political science to automatically label topics. These labels can be used instead of human labeling or supplementing it by guiding the labeling process in a more replicable procedure by retaining humans in the loop. We demonstrate our approach with a large scale topic model analysis of the complete corpus of UK House of Commons speeches 1935-2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluate our results using human expert coding. We show that our approach works well for a majority of the topics we estimate; but we also find that institution-specific topics, in particular on subnational governance, require manual input.
Around the world, cross-sectoral collaborations between universities and the public sector are the norm for leveraging data science and artificial intelligence based capabilities to deliver policy and shape efficiencies in highly uncertain environments. In line with this vision, the HEFCE funded Catalyst Project brought together Essex County Council, Suffolk County Council, and University of Essex to enable innovative and far reaching responses to pressing national and local issues. While such cross-sectoral collaboration is not new, there is a lack of a systematic review of the empirical evidence about which managerial strategies help overcome the serious challenges involved with interorganisational collaboration. This talk presents the first results from a programme of study to take stock of the lessons learnt in the project around collaboration between public authorities and the University and to place these in the context of global best practices in cross-sectoral collaboration.
Political science scholars working with large quantities of textual data are often interested in discovering latent semantic structures in their document collections. Examples include legislative debates, policies, media content, manifestos, and open-ended survey questions. Domain idiosyncrasies often do not allow direct application of standard NLP toolkit. This talk will introduce several recent applications in the areas of climate change politics, international relations, legislative politics, and armed conflict prediction.
AI FOR THE PEOPLE: AI Bias, Ethics & The Common Good. What is the role of AI in improving public services? What is the role of academia in the process? How can we improve collaboration between academia and government to tackle challenges of public service delivery with data science and AI?
The transition from the Millennium Development Goals (MDGs) to the Sustainable Development Goals (SDGs) brought with it significant changes in the process of creating the goals and with the actual content of the SDGs. One of the most important developments was the inclusion of SDG 16, which recognises the central role of effective, accountable and inclusive political institutions in promoting sustainable development. Yet, a significant shortcoming is the difficulty in measuring progress on this SDG 16. In addition to general issues linked with data availability across the various indicators, a key challenge is aggregating trends across these wide-ranging indicators to track overall progress on SDG 16. A second issue that follows, is that despite claims regarding the centrality of SDG 16 for achieving the other SDGs, little is known about the causal pathways from the different indicators in SDG 16 to the other SDGs and associated indicators. In other words, questions remain over how changes in SDG 16 indicators impact a country’s progress towards indicators linked to health, gender equality, water and sanitation, and climate change.
The talk covers the application of data science methods and tools for complex systems as enabler of new science. The Cynefin Framework is introduced as a practical approach to complex systems. The enabling role of machine learning and natural language processing is discussed in the context of the social care system.
Climate change is undermining the foundations of good health; threatening the food we eat, the air we breathe, and the hospitals and clinics we depend on. However, the response to climate change could be the greatest global health opportunity of the 21st century. The Lancet Countdown: Tracking Progress on Health and Climate Change brings together 35 leading academic institutions and UN agencies from every continent to monitor this transition from threat to opportunity. We track annual indicators of progress, empowering the health profession and supporting policymakers to accelerate their response. In the talk we discuss the application of machine learning and natural language processing to develop and track a set of Lancet Countdown indicators.
This course integrates prior training in quantitative methods (statistics) and coding with substantive expertise and introduces the fundamental concepts and techniques of Data Science and Big Data Analytics. Typical students will be advanced undergraduate and postgraduate students from any field requiring the fundamentals of data science or working with typically large datasets and databases. Practitioners from industry, government, or research organisations with some basic training in quantitative analysis or computer programming are also welcome. Because this course surveys diverse techniques and methods, it makes an ideal foundation for more advanced or more specific training. Our applications are drawn from social, political, economic, legal, and business and marketing fields.
Machine learning is a core technology of artificial intelligence (AI) that enables computers to act without being explicitly programmed. Recent advances in machine learning have given us, inter alia, self-driving cars, AlphaGo, Amazon, and Netflix. This technology has also allowed us to predict armed conflict and post-electoral violence, detect fake news, develop targeted provision of care and public services, and implement early policy interventions.
Fake news, disinformation, and propaganda influence elections and increase political polarisation. Parliamentary debates, party manifestos, and public engagement provide for informed politics, robust democracy, and improved public policy. Increasingly large volumes of textual data underpin both of these trends. This course covers theoretical concepts of treating text as data in the context of social science research. The course also provides a hands-on introduction to statistical methods to generate insights from text data using open source libraries in R.
Artificial Intelligence, Machine Learning, and Data Science have been dominating the headlines in the last few years. But what does it all mean? What are these technologies and how are they linked? What benefits can organisations and businesses derive from deploying such technologies and how can they go about and embed them to deliver tangible benefits? What are the governance implications of artificial intelligence deployment? This course aims to demystify these concepts and highlight direct business and societal benefits. Navigating through the complex maze of these rapidly evolving technologies can be non-trivial for organisations of different size and market maturity. We look beyond the hype and focus on the real challenges and opportunities of practical applications of such technologies for organisations. Whether it is to gain efficiency in current business model or transform decision making and product or service delivery, there are many ways to utilise artificial intelligence technologies. We also consider challenges and opportunities arising from ethical, fair, transparent, and accountable deployment of artificial intelligence.
Innovations in Artificial intelligence (AI) are transforming economies and societies globally, and with them politics. This course explores these transformations and corresponding policy challenges. As a governance school Hertie has a special responsibility to address these critical topics. Integrating perspectives from both natural and social sciences, this course will provide learning experiences that examine the impact of AI on humans and societies. We will explore the proliferation of algorithmic decision-making and autonomous systems; the issues of ethics, fairness, transparency and accountability raised by AI techniques such as machine learning; balances and interactions between regulation and innovation; the effects of AI on human rights and economic wellbeing; the global AI arms race; and increasing oppressive capabilities of state- and non-state actors. We consider both public and private strategies of regulation, and local, national, and transnational aspects of governance.
Natural Language Processing (NLP) is a key technology of the information age. Automatically processing natural language outputs is a key component of artificial intelligence. Applications of NLP are everywhere because people and institutions largely communicate in language. Recently statistical techniques based on neural networks have achieved a number of remarkable successes in natural language processing leading to a great deal of commercial and academic interest in the field. This course provides an overview of modern data-driven models to richer structural representations of how words interact to create meaning. We will discuss salient linguistic phenomena and successful computational models. We will also cover machine learning techniques relevant to natural language processing.