The Human in the Data
The Human in the Data initiative focuses on the ubiquity of Big Data and algorithmic decision making in every major sector in our world. Experts from different fields apply sophisticated algorithms to extract insights from data sets, and expectations are high that these insights will aid decision making in areas such as health, food, energy, education, environment and social policy. The use of quantitative data for decision-making has 19th-century roots, but in the 20th and 21st centuries, new technology has enabled more complex collection and application of large datasets. When represented by numbers and numerical proxies, individuals and social relationships are abstracted and obscured to both researchers and end users. Yet data is used to explain, predict, and direct human behavior and define research agendas. Join us as we seek to critically engage with the humanistic dimensions of this significant cultural shift.
Beginning in 2016, the IAS, in partnership with the UMN Informatics Institute and Research Computing, began a joint project originally called Where is the Human in the Data? The partnership has since expanded to include the Digital Arts, Sciences, & Humanities (DASH) program and the UMN Libraries, and is now known simply as the Human in the Data. This project works through joint efforts to bring to the forefront the efforts undertaken by the humanities and social sciences in large data projects. These have included bringing in speakers as part of public events at least annually, as well as providing summer fellowships for graduate student research.
5x5 Human in the Data cohort, 2019-2020:
Lucy Fortson, Physics and Astronomy
Richard Landers, Psychology
Thomas Pengo, Informatics Institute
Benjamin Wiggins, History
Lana Yarosh, Computer Science
Our 5x5 Initiative brings together small groups of people from differing disciplinary backgrounds and positions in the University and off-campus communities for a low-stakes, short-term exploration.
The Human in the Data MnDRIVE Fellowship Program
Eight graduate student summer fellowships in the amount of $7,000 each have been awarded to fund research on the humanistic implications of data and its use in one of five MnDRIVE areas of concentration: robotics, global food, environment, brain conditions, or cancer clinical trials. This fellowship is intended to fund non-traditional scholarship and engagement work that might not normally fit within standard disciplinary graduate research. Students in the humanities, arts, and humanistic social sciences were particularly encouraged to apply. Learn more about the fellowship.
Manami Bhattacharya | “The effect of mental illness on outcomes among older women with breast cancer”
Health Policy and Management, Humphrey School of Public Health
Yuming Fang | Empowered social bots: Content analysis of bots-created anti-vaccine information on Twitter
Hubbard School of Journalism and Mass Communication, College of Liberal Arts
Johnathan Hardy | Ornithological Empire: A Spatio-Analytical Approach to Mapping the Qing Dynasty’s Compendium of Birds (鳥譜, niaopu) (1736 – 1795 CE)
Department of Art History, College of Liberal Arts
Milica Milic-Kolarevic | Law of Gift Gratitude: Serbian Oncology Practices
Department of Anthropology, College of Liberal Arts
Jennifer Nicklay | Community Land and Food Lab Meetings: Facilitating Co-Learning Spaces for Urban Agriculture in Minneapolis/St. Paul, MN
Department of Soil, Water, and Climate, College of Food, Agricultural, and Natural Resource Sciences
Prerna | Aerial Eyes 1
Department of Art, College of Liberal Arts
Hayden Teachout | Aerial Eyes 1
Department of Art, College of Liberal Arts
Sultan Toprak Oker | Harmony of Discordance: Machine Vision, Alcohol Networks, and Taverns
Department of History, College of Liberal Arts
Rebecca Walker | Critical Cartography for Urban Environmental Planning: Exploring the role of participatory mapping in the quest for environmentally just cities
Humphrey School of Public Affairs
April 2, 2019
The Real Promise of AI:
How to Get AI-Human Collaboration to Work
Adaptive Systems and Interaction Group, Microsoft
While many celebrated efforts in Artificial Intelligence aim at exceeding human performance, the real promise of AI in real-world domains, such as healthcare and law, hinges on developing systems that can successfully support human experts. In this talk, Ece Kamar shares several directions of research her team at Microsoft is pursuing towards effective human-AI partnership in the open world, including combining the complementary strengths of human and machine reasoning, addressing concerns around trust, transparency and reliability, and using AI to improve human engagement.
February 7, 2018
Auditing, Explaining, and Ensuring Fairness in Algorithmic Systems
Data & Society Research Institute, Haverford College
Machine learning models are becoming increasingly opaque to human examination, even to their designers. Yet these models are also increasingly used to make high-stakes decisions; who goes to jail, what neighborhoods police deploy to, and who should be hired for a job. But how can we practically achieve accountability and transparency in the face of increasingly complex models? And how do we know if the algorithmic decisions are fair or discriminatory—what does it mean for an algorithm to be fair? Sorelle Friedler discusses work from the new and growing field of Fairness, Accountability, and Transparency in machine learning, examines societal notions of fairness and non-discrimination, and explains how these notions have been defined using a mathematical framework. She also discusses recently developed strategies for auditing black-box models when given access to their inputs and outputs and for white-box interpretability in decision-making.
September 21, 2019
How Spatial Polygons Shape Our World:
Geometry, Data, and Perceptions of Truth
Computer & Information Sciences, University of St. Thomas
Borders often do not have much to do with the physical world. The edges of voting districts, cities, counties, states, and countries are decided by human processes, always implicitly if not explicitly political. Data are often provided preaggregated at a particular spatial polygon level. For example, data on poverty is collected at the blockgroup level, while data on education is easiest to obtain for school districts. This makes it difficult to combine data, and can lead to problems when data does not make sense at the level it was collected. In this talk, Amelia McNamara discusses issues related to spatial polygon choices, like gerrymandering and the Modifiable Areal Unit Problem.
May 4, 2017
Arab Future Trippings and Algorithmic Vision
Laila Shereen Sakr or, VJ Um Amel
Film and Media Studies, University of California Santa Cruz
Whether studying, designing or using algorithms, researchers need to understand how their questions intersect with the logics of automation and scale underpinning networked, computational platforms. In this lecture, VJ Um Amel presents a hybrid approach to analyzing various procedural algorithms, their relationship with their structured data (for example, tweets), and their impact on an Arabic-speaking virtual body politic. This investigation theorizes mediations of Middle Eastern activism, revolution, and migration. It begins by working through the challenges in producing knowledge that is analytically rigorous, durable, and is independent from various power centers and policy circles, securitized and militarized—and then exploring the emergence of new modes of knowledge production in an era of virtual reality, artificial intelligence, and large-scale violence.
October 3, 2016
10 Questions for Critical Data Studies Workshop
Inclusiveness in smartphone apps, gender transition and financial surveillance of identity, workplace surveillance among the world’s top retailers . . . this workshop werved as the launching point for a critical data science study project that was jointly sponsored by the University of Minnesota Informatics Institute and the Institute for Advanced Study for the 2016 cohort of Human in the Data fellows, who participated in lightning talks and table discussions, developing “10 Questions for Critical Data Studies.” Click through to Youtube for the full schedule of presenters.
Emma Bedor Hiland: (En)coding Inclusiveness in Smartphone Applications for Mentally Disordered Users
This project explores the development of smartphone applications intended to treat mental disorder. Unlike other genres of mobile medical interventions, their creation often includes developers or consultants who have personal experience with mental illness. I suggest this presents a valuable model for inclusivity and diversity in coding.
Deniz Coral: Markets with Many Faces: The Role of Screens in the Financial Imagination
This project explores the humanistic aspect of big data by investigating how the data appears on computer screens is culturally produced and interpreted by financial players. While screens are generally taken for granted either as mediums or background context of finance, this project challenges this perspective by exploring the ways in which financial players engage with their screens are pivotal for the visualization of financial data, which is contingent upon larger relationships of trust and institutional hierarchy.
Alexander Fink: Locating Human Possibility and Aspirations in Social Service Mass Data Collection Systems
This project collaborates with a team of young people in a Youth Participatory Action Research study to critically investigate and understand the impacts of big data collection in social services on their lives, especially their sense of future possibility and aspirations.
Amelia Hassoun: Big Data, Big Futures: Imagining the Singaporean Smart Nation
My dissertation project analyzes the production of Singapore as the world’s first truly ‘smart city’: a state-space enacting a data-driven future. Through ethnographic study, I examine how big data gathered from civic technologies inscribed in the urban fabric enumerates Singapore’s exceptionally diverse citizenry and brings imagined futures into being.
Katelin Krieg: Victorian Data Analysis and Visualization
We assume that we have little to learn from Victorian Britain about data analysis and visualization. However, the correlation coefficient and scatterplot both emerged during this period. I juxtapose their inventors, Karl Pearson and Francis Galton, with novelist George Meredith to argue that these statistical and literary representations develop from the same philosophical concerns and had the same knowledge goals.
Alicia Lazzarini: Critically Expanding ‘Data’: Methods for Examining a Southern African Sugar Success
My research expands the notion of data. Mobilizing qualitative, ethnographic and archival methods, I examine a sugar mill’s expansion data and development claims. Addressing labor and land, I analyze disconnects between industry and resident views of investment. I argue: development decisions must be made through diverse, not narrow information forms.
Lars MacKenzie: Accounting for Change: Big Data, Gender Transition and Financial Surveillance of Identity
Big data has transformed financial services, enabling massive collection and networking of consumer data. This research examines how financial institutions manage data about transgender people who change their names, investigating how humans are produced, managed, regulated and normalized through data. I demonstrate that data enables multiple forms of discrimination against transgender people.
Stephen Savignano: Interaction / Machination: Thinking Machines through Interactive Computation
My proposed research examines interactive paradigms in computer science, and the significance of understanding computation through interactions rather than algorithms. Anchored in the idea of artificial intelligence, interaction highlights humanizing possibilities and inhuman obstacles to asking, “Where is the human in the data?”
Link Swanson: User interfaces and the epistemology of the new computational cognitive revolution
A recent trend in cognitive science leverages large-scale online databases to study human cognition. I argue that the viability of this approach hinges on a key epistemological concern: the role of the user interface in online data creation. I detail this concern and consider possible ways to address it.
Madison Van Oort: Well-Dressed Data: Workplace Surveillance in the World’s Top Retailers
Data-based workplace monitoring is increasingly crucial to retail companies’ ability to slash labor costs. This ethnographic study of the booming fast fashion industry—which sells high volumes of trendy, cheap clothing—investigates how new management software gathers data about worker performance and shapes front-line employees’ relationships to work.