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Institution

Dutch Ministry of Justice

About: Dutch Ministry of Justice is a based out in . It is known for research contribution in the topics: Poison control & Prison. The organization has 111 authors who have published 153 publications receiving 2764 citations.


Papers
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Journal ArticleDOI
01 Jun 2010-Futures
TL;DR: In this paper, an early warning system (EWS) is used to monitor various developments and to place them within the perspective of future scenarios, without actually predicting the future, this makes it possible to determine which scenario is the most relevant at any given moment.

305 citations

Journal ArticleDOI
TL;DR: It is found that the way data is stored, the wayData is obtained and the way Data is used by a department are crucial indicators for open data release.

260 citations

Book ChapterDOI
TL;DR: Legal issues on frequency Spectrum usage and electronic equipment (national and international legal matters on frequency spectrum and equipment requirements) and surveillance and compliance (enforcement of frequency spectrum use, equipment requirements, and the need for international and European cooperation) are discussed.
Abstract: The different types of drones can be differentiated in terms of the type (fixed-wing, multirotor, etc.), the degree of autonomy, the size and weight, and the power source. These specifications are important, for example for the drone’s cruising range, the maximum flight duration, and the loading capacity. Aside from the drone itself (i.e., the ‘platform’) various types of payloads can be distinguished, including freight (e.g., mail parcels, medicines, fire extinguishing material, flyers, etc.) and different types of sensors (e.g., cameras, sniffers, meteorological sensors, etc.). Applications of different payloads will be described. In order to perform a flight, drones have a need for (a certain amount of) wireless communication with a pilot on the ground. In addition, in most cases there is a need for communication with a payload, like a camera or a sensor. To allow this communication to take place frequency spectrum is required. The requirements for frequency spectrum depend on the type of drone, the flight characteristics, and the payload. Since frequency spectrum does not end at national borders, international coordination on the use of frequency spectrum is required. Legal issues on frequency spectrum usage and electronic equipment (national and international legal matters on frequency spectrum and equipment requirements) are discussed, as well as frequency spectrum and vulnerability (an insight in available frequency spectrum and associated risks in using the frequency spectrum) and surveillance and compliance (enforcement of frequency spectrum use, equipment requirements, and the need for international and European cooperation). Finally, future developments in drone technology are discussed. The trend is for drones to become smaller, lighter, more efficient, and cheaper. As a result, drones will become increasingly available to the public at large and will be used for an increasing range of purposes. Drones will become increasingly autonomous and also more capable of operating in swarms.

170 citations

Journal ArticleDOI
TL;DR: This paper demonstrates empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process.
Abstract: Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models (decision rules) captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms may discriminate people, even if the computing process is fair and well-intentioned. This happens due to biased or non-representative learning data in combination with inadvertent modeling procedures. From the regulatory perspective there are two tendencies in relation to this issue: (1) to ensure that data-driven decision making is not discriminatory, and (2) to restrict overall collecting and storing of private data to a necessary minimum. This paper shows that from the computing perspective these two goals are contradictory. We demonstrate empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process. Of course, after the model is ready, race should not be required as an input variable for decision making. From the regulatory perspective this has an important implication: collecting sensitive personal data is necessary in order to guarantee fairness of algorithms, and law making needs to find sensible ways to allow using such data in the modeling process.

103 citations

Journal ArticleDOI
TL;DR: In this article, prediction mod els are developed that predict three types of criminal recidivism: general recidivisitc, violent re-conviction, and sexual recidivasitc.
Abstract: Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared on a large selection of performance measures. Results indicate that classical methods do equally well as or better than their modern counterparts. The predictive performance of the various techniques differs only slightly for general and violent recidivism, whereas differences are larger for sexual recidivism. For the general and violent recidivism data we present the results of logistic regression and for sexual recidivism of linear discriminant analysis. Language: en

83 citations


Authors

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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20214
20205
20195
20188
201712
201620