accountability for data mining; intelligent traffic; smart cars; cybercrime; data ownership; accountability for data security; data crimes; regulatory framework for big data ; criminal investigation in the digital age; data protection; data law
ZechHerbert (2021),
Liability for AI: public policy considerations, Springer, Berlin.
GlessSabine (2020), AI in the Courtroom: A Comparative Analysis of Machine Evidence in Criminal Trials, in
Georgetown Journal of International Law, 51, 195-253.
ZechHerbert (2020), Besitz an Daten?, in Pertot Tereza (ed.), Mohr Siebeck, Tübingen, 91-102.
ZechHerbert (2020), Digitalisierung – Potential und Grenzen der Analogie zum Analogen, in Eifert Martin (ed.), Nomos Verlag, Baden-Baden, 29-44.
ZechHerbert (2020),
Entscheidungen digitaler autonomer Systeme: Empfehlen sich Regelungen zu Verantwortung und Haftung?, C.H. Beck, München.
ZechHerbert (2020), Risiken digitaler Systeme: Robotik, Lernfähigkeit und Vernetzung als aktuelle Herausforderungen für das Recht, Weizenbaum Series #2 2020, in
Weizenbaum Series, 2, 1-53.
ZechHerbert (2019), Artificial Intelligence: Impact of Current Developments in IT on Intellectual Property, in
GRURInt, 1145-1147.
Schmidt Kirsten Johanna (2019), Datenmärkte ohne «Dateneigentum», in
digma, 4, 178-183.
SchmidtKirsten Johanna (2019),
Datenschutz als Vermögensrecht, PhD thesis Basel 2019, Springer Verlag, Springer, Wiesbaden.
ZechHerbert (2019), Gene Sequence Data between Public Domain and Property – Application of the Nagoya Protocol and the Regulation (EU) No. 511/2014?, in
GRURInt, 453-456.
ZechHerbert (2019), Künstliche Intelligenz und Haftungsfragen, in
Zeitschrift für die gesamte Privatrechtswissenschaft , 198-219.
ZechHerbert (2019), Liability for autonomous systems: Tackling specific risks of modern IT, in Lohsse Sebastian, Staudenmayer Dirk, Schulze Reiner (ed.), Nomos Verlag, Baden-Baden, 185-200.
GlessSabine, WohlersWolfgang (2019), Strafrechtliche Verantwortlichkeit für "smarte" Produkte am Beispiel der Fahrautomatisierung, in
Schweizerische Zeitschrift für Strafrecht, 366-399.
GlessSabine, WohlersWolfgang (2019), Subsumtionsautomat 2.0 – Künstliche Intelligenz statt menschlicher Richter?, in Böse Martin, Toepel Friedrich, Schumann Kay (ed.), Nomos Verlag, Baden-Baden, 147-165.
Möhrke-SobolewskiChristine (2018), AI: How Privacy Impact Assessments serve to minimise risks in projects with artificial intelligence, in
Jusletter IT, 10.
SchmidtKirsten Johanna (2018), Datenschutz und Big Data – Ein Spannungsverhältnis, in Maute Lena, Mackenrodt Mark-Oliver (ed.), Nomos Verlag, Baden-Baden, 265-285.
SchmidtKirsten Johanna (2018), Die datenschutzrechtliche Einwilligung – Ein Instrument zur Kommerzialisierung, aber keine Verfügung, in
GRUR Newsletter, 2, 14-16.
GlessSabine, StagnoDario (2018), Digitale Assistenten und strafprozessuale Beweisführung, in
Schweizerische Juristenzeitung, 114, 289-297.
SchmidAlain (2018), Münster Colloquia on EU Law and the Digital Economy - Trading Data in the Digital Economy: Legal Concepts and Tools vom 4. und 5. Mai 2017, in
Zeitschrift für Europäisches Privatrecht, (1), 292-296.
GlessSabine (2018), Predictive Policing – In Defense of ‘True Positives’, in Janssens Liisa, Baraliuc Irina, Hildebrandt Mireille, Bayamlıoğlu Emre (ed.), Amsterdam University Press, Amsterdam, 76-83.
Zech Herbert, Schmid Alain, Schmidt Kirsten Johanna (2018), Rechte an Daten – zum Stand der Diskussion, in
sic!, 11, 627-639.
GröflinAlexander (2018),
Web observations: analysing Web data through automated data extraction, Universität Basel, Basel.
Gless Sabine (2018), Wenn das Haus mithört – Beweisverwertungsverbote im digitalen Zeitalter, in
Strafverteidiger, 10, 671-678.
Zech Herbert (2017), Building a European Data Economy - The European Commission's Proposal for a Data Producer's Right, in
Zeitschrift für Geistiges Eigentum, 9(3), 317-330.
Graf Melanie, Schmidt Kirsten Johanna (2017), Data Mining und wissenschaftliche Forschung - de lege lata und de lege ferenda, in
sui generis, 185-200.
ZechHerbert, SchmidtKirsten Johanna (2017), Datenbankherstellerschutz für Rohdaten?, in
Computer und Recht online, (7), 417-426.
Gless Sabine (2017), Von der Verantwortung einer E-Person, in
Goltdammer’s Archiv für Strafrecht , 164(6), 324-329.
GlessSabine (2017), Zur Aktualität von Vergessen und Vergeben im digitalen Zeitalter, in
Goltdammer’s Archiv für Strafrecht, 164(5), 254-259.
Harvesting information from huge amounts of raw data, i.e. Big Data, holds new promises, for society as a whole as well as for individuals, especially those doing business that profits from the digital revolution. At the same time Big Data carries new risks and poses novel challenges, not least for legal systems. It is, for instance, yet unclear how to allocate benefits, especially the value represented by raw or by refined data, how to avert risks for privacy and autonomy underpinning many legal institutions, or how to enhance security issues in Big Data processing with the use of legal instruments, for instance efficient criminal prosecution that ought to protect privacy as well as security in the digital age. At present, law arguably cannot properly perform its regulatory function since it has not yet come up with a fitting regulative framework. In this project “intelligent traffic” is used as a case study and example for illustrating in an interdisciplinary approach of law and computional science the benefits and the risks of Big Data. The case study will include “smart cars” as well as car sharing services, where Big Data presumably will have one of the first huge impacts on everyday live.The project aims at (a) identifying the conflicts of interests relevant for the legal regulation of Big Data, (b) elaborating doctrinal approaches that adequately underpin a legal framework governing Big Data use, (c) proposing a new regulatory framework that effectively allocates legal entitlements as well as best averts risks in a future where not only businesses, but also society, or rather state authorities, will base decisions on data mined information. A group of four young researchers shall cover crucial legal topics concerning allocation of benefits and avoidance of risks and, under the supervision of faculty members and networking with external experts from the different fields involved in “Intelligent Traffic”. The young researchers’ goal is to provide PhD theses that will cover: (1.) “Big Data and property rights - how to share the benefits?”, (2.) “Big Data and consumer protection - beyond conventional privacy concepts”; (3.) “Big Data and criminal investigations - the right to privacy and other privileges”; (4.) “Guardians of (Big) Data - criminal justice obligations and entitlements”.Overall research findings of the project team shall be published in leading journals (nationally and internationally) and be shared during two conferences and a final symposium with the various stakeholders and the public.