The Future of Prediction: The Social Consequences of Algorithmic Forecast in Insurance, Medicine and Policing

Información del proyecto

PREDICT

Identificador del acuerdo de subvención: 833749

DOI

10.3030/833749

Proyecto cerrado

Fecha de la firma de la CE 18 Julio 2019

Fecha de inicio 1 Febrero 2020

Fecha de finalización 31 Enero 2026

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 2 084 429,00

Aportación de la UE

€ 2 084 429,00

Coordinado por

UNIVERSITAET BIELEFELD
Germany

Periodic Reporting for period 4 - PREDICT (The Future of Prediction: The Social Consequences of Algorithmic Forecast in Insurance, Medicine and Policing)

Período documentado: 2024-08-01 hasta 2026-01-31

Resumen del contexto y de los objetivos generales del proyecto

The algorithmic turn of prediction, connected with big data and machine learning, presents an exciting and urgent challenge for the social sciences. We are facing a radical technical-computational innovation, whose effects extend throughout society, promising innovative solutions and yet producing disruptive problems.
To address these challenges, the project carries out a set of theory-driven empirical studies of the transition from probabilistic forms of uncertainty management to the new algorithmic forms of prediction in three important social areas – insurance, medicine and policing.
In the field of insurance, Insurtech promises precise and individualized risk forecasting, that should allow companies to know the specific needs and characteristics of their customers and to propose personalized offers, messages, pricing and recommendations. The problem, however, is that individualization could undermine the mutualization principle on which insurance is based, with the risk of inefficiency and discrimination, and even of emptying its business model.
In the medical field, Precision Medicine promises to turn healthcare from a reactive to a proactive field by enabling the prediction of future needs of patients and the customization of medical interventions according to such highly specific predictions. This should allow to overcome the wastes and inefficiencies inherent in the current "one-size-fits-all" approach. The problem is that personalization must be combined with established approaches and methods in medical practice (both in the clinic and in research), that address large populations - with inevitable consequences for the experience of patients and staff, as well as for scientific activity and institutions.
In the field of crime prevention, algorithmic prediction has led to the development of Predictive Policing, applying digital techniques that claim to identify potential criminal activities and to intervene before they are accomplished. Predictive methods are expected to allow police to move from a reactive to a proactive approach. But this attitude does not take into account a fundamental distinction that our research is addressing: that prediction by itself is not prevention. Our analysis is focuses on the relationship between prediction and prevention and on its consequences for policing activity.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

Our research was conducted in a coordinated manner across three distinct social domains.

In the field of insurance, during the course of the project we carried out the first comprehensive study on the impact of algorithmic technologies, empirically assessing the scope and limitations of so-called InsurTech innovations. During a long and fruitful collaboration with SwissRe, we investigated in detail the collection and use of behavioral data in the motor insurance sector, studying the impact on forms of assessment and insurance solidarity, as well as the shifting meaning of engagement and proactivity. We then extended the investigation to the life and health insurance sector, empirically examining experimentation with advanced forms of personalization, their limits, and their consequences. This work has led to 5 publications in international journals, our organization of two international conferences, and our participation as speakers at many other conferences and workshops. We are deeply embedded in the community of social studies of insurance.

In the field of medicine, we conducted an in-depth investigation into how the emphasis on precision medicine has translated into concrete innovations for clinical practice and research. Based on a thorough conceptual analysis, we ethnographically studied the activities of Molecular Tumor Boards, where innovative forms of prediction are put into practice, and we investigated the trend toward unprecedented forms of temporal precision. We explored the implications of algorithmic techniques in epidemiology, analyzing the potential separation between clinical epidemiology and population epidemiology in an article published in one of the most important journals in the field of epidemiology. We have published 5 articles in leading international peer-reviewed journals, including one in the most important journals in the field of the sociology of medicine, and we have presented our work at numerous conferences in the field.

In the field of policing, we have empirically studied how the availability of predictive tools affects the preventive activities of police units and their impact on the community, conducting extensive ethnographic research that has resulted in the presentation of our work at numerous conferences and workshops. We conducted the first research on the reception and use of Palantir in Europe (starting with the example of Germany) and the related debate. Our work, presented at numerous international conferences (one of which we organized), has resulted in 8 articles in international journals, 4 book chapters, the co-editing of a collective book, and the co-authorship of another.

In the General Project, the work on the project led to significant conceptual insights into the notion of artificial intelligence in two books that explore an alternative approach to artificial communication. We explored the broader transformation of forms of prediction related to the impact of algorithms in 12 articles on the notions of open future, future-making, preparedness, and the relationship between search engines and answer engines. The PI has presented her work on artificial communication and algorithmic prediction at many international conferences and talks, often as a keynote speaker, and organized two major international conferences in New York titled The Future of Prediction, at Columbia Business School and at Cornell Tech.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

In all areas of the project, our research has revealed the emergence of novel ways of combining probabilistic and algorithmic forms of prediction, which take on complex and diverse forms across different fields. Contrary to the widespread expectation that the accuracy of algorithmic forecasts would supplant current general and impersonal forecasts, our research has shown the emergence in our three fields of study of innovative forms of direct intervention in the future that complement established methods of managing uncertainty: actionability in medicine, proactivity in insurance, and preemption in policing. We have documented the ongoing experimentations to manage and implement the results, which are still open-ended. These findings go far beyond the standard juxtaposition between generalized forecasting and innovative forms of personalized forecasting, and have proven to be extremely useful in concretely analyzing ongoing developments.