Max Planck Law-Tech-Society
- Christian Boulanger (MPI Frankfurt)
- Nina Grgić-Hlača (MPI Bonn)
- Yoan Hermstrüwer (MPI Bonn)
- Anselm Küsters (MPI Frankfurt)
- Alexandra Woods (MPI Frankfurt)
- Andreas Wagner (MPI Frankfurt)
Over the last decade, research on “law and technology” has exploded, cutting across all legal disciplines. Indeed, an increasing number of Max Planck Law scholars are contributing to this field, examining the legal, regulatory, policy, and social implications of emerging technologies and exploring the opportunities technologies offer as a methodological tool for legal research and practice. In parallel, Max Planck Institutes in the MPG’s Chemistry, Physics & Technology Section are leading the development of new technologies that will continue to shape our societies.
Given the rapid pace of today’s global technological development and its wide-ranging societal impact, collaboration and interdisciplinary exchange of thought is a prerequisite to grasp the complexity and multiple dimensions of the field and for producing cutting edge research. It is also essential to enable research that actively and effectively shapes public discourse on legal, ethical, and social implications of various emerging technologies and their regulation.
The Max Planck Law-Tech-Society Initiative will serve as a hub for the exchange of ideas on common themes, concerns, and challenges raised by the complex and interdependent relationships between law, technology, and society.
It will do so by: 1) strengthening interdisciplinary collaboration and realising synergies between researchers from across Max Planck Law Institutes and Max Planck Institutes in the MPG’s Chemistry, Physics & Technology Section; (2) providing a forum to those eager to learn more about this field, for example, by providing guidance and support to Max Planck Law researchers who are interested in learning about how to apply new digital research techniques; and (3) fostering network-building with other leading institutions in the field.
Once established, the Initiative will help to increase the visibility of technology-related research at Max Planck Institutes, cement their leadership in the field, and attract new international talent.
Fair Governance with Humans and Machines
Yoan Hermstrüwer (MPI-Bonn)
10 Dec, 15:00 – 16:00 CET
How fair are government decisions based on algorithmic predictions? And to what extent can the government delegate decisions to machines without sacrificing procedural fairness? Using a set of vignettes in the context of predictive policing, school admissions, and refugee matching, we explore how different degrees of human-machine interaction affect fairness perceptions and procedural preferences. We implement four treatments varying the extent of responsibility delegation to the machine and the degree of human involvement in the decision making process, ranging from full human discretion, machine-based predictions with high human involvement, machine-based predictions with low human involvement, and fully machine-based decisions. We find that machine-based predictions with high human involvement yield the highest and fully machine-based decisions the lowest fairness score. These differences can partly be explained through different accuracy assessments. Fairness scores follow a similar pattern across contexts, with a negative level effect and lower fairness perceptions of human decisions in the predictive policing context. Our results shed light on the behavioral foundations of several legal human-in-the-loop rules.
15 January 2021, Datafying the Law
On 15 January (2:00-3:30 pm) the Law – Tech – Society Initiative hosted its 1st Online Workshop. Senior Research Fellow Hanjo Hamann (MPI for Research on Collective Goods) was our guest speaker and gave a presentation on Datafying the Law: On Appropriate Ambitions for Legal Techies.
31 March 2021, Virtual Cafe
With Ilka Schiessler-Gäbler and Yoan Hermsüwer, Max Planck Society
At the Virtual Café, Falling Walls Lab alumni are introduced to renowned German research institutions and are given exclusive peaks into the projects and programmes led by the respective institutions.
7 May 2021, Citizen Participation and Machine Learning for a Better Democracy
Rob Procter (Warwick University and Alan Turing Institute), Miguel Arana Catania (Warwick University and Alan Turing Institute), Felix-Anselm van Lier (MPI for Social Anthropology) present their work on the use of machine learning tools in digital democracy processes. More information:
Around the world, digital participation platforms are being used as a tool for direct democracy, aiming to empower citizens to contribute to policy making. As trust in traditional democratic institutions declines, these deliberative platforms offer a way to build new relationships and trust between citizens and policy-makers.
However, experience suggests that the popularity of these direct democracy initiatives and the sheer volume of proposals can make it difficult for citizens and policy-makers to make sense of them. Hence, common objectives may prove difficult to achieve; paradoxically, the consequence may be to reduce the willingness of citizens to get involved in direct democracy initiatives.
This talk presents the outcomes of a study conducted on the world’s most popular digital participation platform, Consul, and explain how Natural Language Processing and machine learning can be used to address existing challenges of citizen participation platforms. On the basis of these results, the speakers sketch the opportunities and challenges of such tools for citizen engagement in policy- and law-making processes at different state levels.
24 June 2021, Exploring historical analogies in ECB speeches through Text Mining (1997-2019): some methodological remarks
Presentation by Anselm Küsters (Researcher, MPI-Frankfurt)
In recent work (an early working paper version can be found here), I employed so-called text mining methods such as structural topic modeling to examine all 2,135 speeches by ECB Executive Board members between February 1997 and October 2019. These new methods from the rapidly growing field of digital humanities allowed me to identify and analyze a significant semantic change that occurred in ECB communication in the transition from Great Moderation to Great Recession. The methodology also allowed for a structured and empirical assessment of the hypothesis that central bankers used ‘lessons from the past’ during the crisis. The quantitative and qualitative results indicate that references to historical analogies indeed increased at the height of the crisis (2009–11) but often served only rhetorical functions. In my talk, I will present some of the text mining methods that I have applied as part of this research and then use the empirical results as a starting point for a broader methodological discussion, reflecting on how digital humanities approaches are changing our research designs, and how the results obtained using these new methods differ from those obtained via traditional close reading.
As usual in this discussion series, a short summary of the main theses will be circulated among the registered participants a few days before the talk. It is not necessary to read the full working paper.
22 October 2022, Learning from the People: Responsibly Encouraging Adoption of Contact Tracing Apps
Elissa Redmiles (MPI Saarbrücken)
At the beginning of the pandemic contact tracing apps proliferated as a potential solution to scaling infection tracking and response. While significant focus was put on developing privacy protocols for these apps, relatively less attention was given to understanding why, and why not, users might adopt them. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps. In this talk I showcase the value of taking a descriptive ethics approach to setting best practices in this new domain. Descriptive ethics, introduced by the field of moral philosophy, determines best practices by learning directly from the user — observing people’s preferences and inferring best practice from that behavior — instead of exclusively relying on experts’ normative decisions. This talk presents an empirically-validated framework of user’s decision inputs to adopt COVID19 contact tracing apps, including app accuracy, privacy, benefits, and mobile costs. Using predictive models of users’ likelihood to install COVID apps based on quantifications of these factors, I show how high the bar is for achieving adoption. I conclude by discussing a large-scale field study in which we put our survey and experimental results into practice to help the state of Louisiana advertise their COVID app through a series of randomized controlled Google Ads experiments.