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.
Kurt Stockinger was invited to contribute a blog to ACM SIGMOD – the leading world-wide community of database research. The blog discusses recent technological advances of natural language interfaces to databases. The ultimate goal is to talk to a database (almost) like to a human.
The full blog can be found on the following ACM SIGMOD link:
In a lecture for the Fair Data Forum, I dealt with the question “What value does data protection have for individuals and what are they willing to pay for it?”
The three data privacy types
As always, there is not one “individual”, as everyone has different data protection preferences and thus, attributes different value to having personal data safeguarded. Therefore, in order to classify individuals, there are different “typologies”. For example, Westin distinguishes between data protection fundamentalists, data protection pragmatists and completely unconcerned individuals. In 2002, Sheehan (2002) selected 889 persons in the USA and classified them with a questionnaire. Conclusion: 16% of the respondents were completely unconcerned about data protection, 81% were classified as pragmatists, and 3% as fundamentalists.
The aim of the PhD Network in Data Science is to offer students with a master degree (including degrees from an university of applied sciences) the opportunity to obtain a PhD in cooperation between a university of applied sciences and a university.
The PhD Network in Data Science is supported by Swissuniversities. It is a cooperation between three departments of ZHAW Zurich University of Applied Sciences (School of Management and Law, Life Science and Facility Management, School of Engineering), three departments of the University of Zurich (Faculty of Science, Faculty of Business, Economics and Informatics, Faculty of Arts and Social Sciences), the Faculty of Science at the University of Neuchatel and the Department of Innovative Technologies at SUPSI University of Applied Sciences and Arts of Southern Switzerland.
PhD students work in applied research projects at the university of applied sciences and are supervised jointly by a supervisor at the university and a co-supervisor at the university of applied sciences. They are enrolled in the regular PhD programs of the partner universities and have to go through the standard admission procedure. After successful completion they receive the doctorate of the respective partner university (UZH or UNIBE). The PhD Network is also open to students with a master’s degree from a university of applied sciences. They, however, have to go through convergence programs (specific to the respective faculty) for admission to the partner universities.
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.
In 2014, ZHAW Datalab started the SDS conference series. It was the year with only one Swiss data scientist identifiable on LinkedIn (at Postfinance…). The year where we talked about “Big Data”, and not “Digitization”. The year where we were unsure if such a thing as a Swiss data science community would exist, and if it actually would come to such an event.
SDS grew from a local workshop to a conference with over 200 participants and international experts as keynote speakers in 2016. This was the year where finally a Swiss-wide network of strong partners form academia and industry emerged to push innovation in data-driven value creation: the Swiss Alliance for Data-Intensive Services (www.data-service-alliance.ch). We as datalabbers have been instrumental in founding this alliance, and then found it to be the perfect partner to take this event to the next level of professionalism.
In this blog post I will show how to combine dynamic models from Modelica with reinforcement learning.
As part of one of my master projects a software environment was developed to examine reinforcement learning algorithms on existing dynamic models from Modelica in order to solve control tasks. Modelica is a non-proprietary, object-oriented, equation based language to conveniently model complex physical systems .
The result is the Python library Dymola Reinforcement Learning (dymrl) which allows you to explore reinforcement learning algorithms for dynamical systems.
Several additional organisations sponsored and supported the conference to give it a successful execution – the organising committee thanks IT Logix & Microsoft, PwC, Google, Zühlke, SGAICO, Hasler Stiftung and the Swiss Alliance for Data-Intensive Services for their support in bringing together a successful event! Continue reading
In this blog I explore the possibility to use a trained CNN on one image dataset (ILSVRC) as feature extractor for another image dataset (CIFAR-10). The code using TensorFlow can be found at github. Continue reading
In this article, I recount measures and approaches used to deal with a relatively small data set that, in turn, has to be covered “perfectly”. In current academic research and large-scale industrial applications, datasets contain millions to billions (or even more) of documents. While this burdens implementers with considerations of scale at the level of infrastructure, it may make matching comparatively easy: if users are content with a few high quality results, good retrieval effectiveness is simple to attain. Larger datasets are more likely to contain any requested information, linguistically encoded in many different ways, i.e., using different spellings, sentences, grammar, languages, etc.: a “blind shot” will hit a (one of many) target.
However, there are business domains whose main entities’ count will never reach the billions, therefore inherently limiting the document pool. We have recently added another successfully finished CTI-funded project to our track record, which dealt in such a business domain. Dubbed “Stiftungsregister 2.0”, the aim of the project was to create an application which enables users to search for all foundations in existence in Switzerland.