Law Archive

Data protection – are we really paradoxical?

translated from the original German language version published at Inside IT

A common narrative in practice sounds something like this: “people claim data protection is important to them, but in reality they give away everything on the internet anyway”. There are also some science studies that seem to prove this again and again: that we are generally careless with our and other personal data and that we consider data protection important but neglect it in everyday life. For example, a “pizza experiment” with 3,000 students at a US university in 2017 concluded that a free pizza was enough of an incentive to reveal the email addresses of three fellow students (Athey et al. 2017).

Algorithmic Fairness – Algorithms and Social Justice

5. February 2020 / mild / 0 Comments

By Christoph Heitz (ZHAW)

translated from original German language version published at Inside IT

Can a prisoner be released early, or released on bail? A judge who decides this should also consider the risk of recidivism of the person to be released. Wouldn’t it be an advantage to be able to assess this risk objectively and reliably? This was the idea behind the COMPAS system developed by the US company Northpoint.

The system makes an individual prediction of the chance of recidivism for imprisoned offenders, based on a wide range of personal data. The result is a risk score between 1 and 10, where 10 corresponds to a very high risk of recidivism. This system has been used for many years in various U.S. states to support decision making of judges – more than one million prisoners have already been evaluated using COMPAS. The advantages are obvious: the system produces an objective risk prediction that has been developed and validated on the basis of thousands of cases.

In May 2016, however, the journalists’ association ProPublica published the results of research suggesting that this software systematically discriminates against black people and overestimates their risk (Angwin et al. 2016): 45 percent of black offenders who did not reoffend after their release were identified as high-risk. In the corresponding group of whites, however, only 23 percent were attributed a high risk by the algorithm. This means that the probability of being falsely assigned a high risk of recidivism is twice as high for a black person as for a white person.

Study on “Quantified Self” Published: Links to Book and Summary

9. April 2018 / mild / 1 Comment

By Kurt Stockinger (ZHAW)

The final results of an interdisciplinary study funded by „TA Swiss“ on „Quantified Self“ with participation of the Datalab have been published. The study was performed by three ZHAW departments (School of Health Professions, School of Management and Law, School of Engineering) in cooperation with the Institute for Futures Studies and Technology Assessment, Berlin. The focus of the Datalab was on legal and Big Data aspects of quantified self.

The results are available in various forms:

A book (for people who love reading)
- https://vdf.ch/index.php?route=product/product/download&eoa_id=9119&product_id=2089
A 24-page summary in four languages (for people who don’t want to read some 250 pages)
A podcast from SRF 1 (Echo der Zeit)
- https://www.srf.ch/play/radio/echo-der-zeit/audio/potenziale-und-risiken-digitaler-selbstvermessung?id=0d7f2fc9-1f86-4233-8ddd-f04c93b73e24
A NZZ article
- https://zeitungsarchiv.nzz.ch/neue-zuercher-zeitung-vom-06-04-2018-seite-22.html?hint=3269165 (subscription needed)

Enjoy reading and maybe you get encouraged to “quantify yourself” a bit better 😉

Search engines in the light of privacy, data protection, freedom of information and the right to be forgotten

25. August 2014 / plik / 0 Comments

Google In this post, our new Datalab members Kurt Pärli and Anita Zimmermann from ZHAW’s Zurich Center for Privacy and Data protection comment on the recent judment of the European court against Google; see also