Ben studies the social and political impacts of government algorithms. This work falls into several categories. First, evaluating how people make decisions in collaboration with algorithms. This work involves developing machine learning algorithms and studying how people use them in public sector prediction and decision settings. Second, studying the ethical and political implications of government algorithms. Much of this work draws on STS and legal theory to interrogate topics such as algorithmic fairness, smart cities, and criminal justice risk assessments. Third, developing algorithms for public sector applications. In addition to academic research, Ben spent a year developing data analytics tools as a data scientist for the City of Boston.
I have been involved in the building of data infrastructure in the study of elections, political systems, violence, geospatial units, demographics, and topography. This infrastructure will eventually lead to the integration of data across many domains in the social, health, population, and behavioral sciences. My core research interests are in elections and political organizations.
My research focuses on the causes, dynamics and outcomes of conflict, at the international and local levels. My methodological areas of interest include spatial statistics, mathematical/computational modeling and text analysis.
Map/time-series/network plot, showing the flow of information across battles in World War II. Z axis is time, X and Y axes are longitude and latitude, polygons are locations of battles, red lines are network edges linking battles involving the same combatants. Source: https://doi.org/10.1017/S0020818318000358
Jeffrey D. Morenoff is a professor of sociology, a research professor at the Institute for Social Research (ISR), and a professor of public policy at the Ford School. He is also director of the ISR Population Studies Center. Professor Morenoff’s research interests include neighborhood environments, inequality, crime and criminal justice, the social determinants of health, racial/ethnic/immigrant disparities in health and antisocial behavior, and methods for analyzing multilevel and spatial data.
Every year, states negotiate, conclude, sign, and give effect to hundreds of new international agreements. Koremenos argues that the detailed design provisions of such agreements matter for phenomena that scholars, policymakers, and the public care about: When and how international cooperation occurs and is maintained.
Theoretically, Koremenos develops hypotheses regarding how cooperation problems like incentives to cheat can be confronted and moderated through law’s detailed design provisions. Empirically, she exploits her data set composed of a random sample of international agreements in economics, environment, human rights and security.
Her theory and testing lead to a consequential discovery: Considering the vagaries of international politics, international cooperation looks more law-like than anarchical, with the detailed provisions of international law chosen in ways that increase the prospects and robustness of cooperation.
Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.
My core research focuses on the politics and measurement of human rights, discrimination, violence, and repression. I use computational methods to understand why governments around the world torture, maim, and kill individuals within their jurisdiction and the processes monitors use to observe and document these abuses. Other projects cover a broad array of themes but share a focus on computationally intensive methods and research design. These methodological tools, essential for analyzing data at massive scale, open up new insights into the micro-foundations of state repression and the politics of measurement.
People rely more on strong ties for job help in countries with greater inequality. Coefficients from 55 regressions of job transmission on tie strength are compared to measures of inequality (Gini coefficient), mean income per capita, and population, all measured in 2013. Gray lines indicate 95% confidence regions from 1000 simulated regressions that incorporate uncertainty in the country-level regressions (see below for more details). In each simulated regression we draw each country point from the distribution of regression coefficients implied by the estimate and standard error for that country and measure of tie strength. P values indicate the simulated probability that there is no relationship between tie strength and the other variable. Laura K. Gee, Jason J. Jones, Christopher J. Fariss, Moira Burke, and James H. Fowler. “The Paradox of Weak Ties in 55 Countries” Journal of Economic Behavior & Organization 133:362-372 (January 2017) DOI:10.1016/j.jebo.2016.12.004
My research is at the intersection of Science of Science + Sociology of Organizations + Computational Social Science. I study how social and organizational factors affect scientific discovery. I am especially interested in evaluation practices in science, and whether they promote or stifle innovation. My approach relies primarily on field experiments — interventions in scientific competitions and other settings — and applying computational tools to large-scale observational data.
Current research projects include:
1. Cumulative advantage in science: Do metrics like citation counts and impact factors proxy quality and influence, or help create them?
2. Biases in expert evaluation: Do groups of experts make decisions differently from individuals?
3. Science and the media: What research is picked up by the media, and how is it covered?
Showing how often a paper has been cited causes scientists to perceive it as of lower quality, unless that paper is among the 10% most highly cited.
I am interested in the intersection of big data, data science, privacy, security, public policy, and law. At U-M, this includes co-convening the Dissonance Event Series, a multi-disciplinary collaboration of faculty and graduate students that explore the confluence of technology, policy, privacy, security, and law. I frequently guest lecture on these subject across campus, including at the School of Information, Ford School of Public Policy, and the Law School.
Yuki Shiraito works primarily in the field of political methodology. His research interests center on the development and applications of Bayesian statistical models and large-scale computational algorithms for data analysis. He has applied these quantitative methods to political science research including a survey experiment on public support for conflicting parties in civil war, heterogeneous effects of indiscriminate state violence, and the detection of text diffusion among a large set of legislative bills.
After completing his undergraduate education at the University of Tokyo, Yuki received his Ph.D. in Politics (2017) from Princeton University. Before joining the University of Michigan as an Assistant Professor in September 2018, he served as a Postdoctoral Fellow in the Program of Quantitative Social Science at Dartmouth College.