By Nico Ebert (ZHAW)
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).
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
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.
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.
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)
- 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)
- A NZZ article
Enjoy reading and maybe you get encouraged to “quantify yourself” a bit better 😉
In this post, our new Datalab members Kurt Pärli and Anita Zimmermann from ZHAW’s Zurich Center for Privacy and Dataprotection comment on the recent judment of the European court against Google; see also