Accountability for the use of algorithms in a big data environment

ABSTRACT

Accountability is the ability to provide good reasons in order to explain and to justify actions, decisions and policies for a (hypothetical) forum of persons or organisations. Since decision-makers, both in the private and in the public sphere, increasingly rely on algorithms operating on Big Data for their decision-making, special mechanisms of accountability concerning the making and deployment of algorithms in that setting become gradually more urgent. In the upcoming General Data Protection Regulation, the importance of accountability and closely related concepts, such as transparency, as guiding protection principles, is emphasised. Yet, the accountability mechanisms inherent in the regulation cannot be appropriately applied to algorithms operating on Big Data and their societal impact. First, algorithms are complex. Second, algorithms often operate on a random group level, which may pose additional difficulties when interpreting and articulating the risks of algorithmic decision-making processes. In light of the possible significance of the impact on human beings, the complexities and the broader scope of algorithms in a big data setting call for accountability mechanisms that transcend the mechanisms that are now inherent in the regulation.     

Measuring the effectiveness of competition in defense procurement: A survey of the empirical literature

This article surveys the empirical literature that has attempted to measure the effects of competition in defense procurement. Its focus is on the conceptual underpinnings of the empirical models rather than on the technical aspects of the estimation procedures. While the empirical studies provide some valuable insight, the studies are flawed because they assume an implicit model of the procurement environment that is inconsistent with reasonable economic behavior on the part of defense contractors and seems to be contradicted by the evidence. In general, the predictive power of the empirical models is also limited by a program-by-program estimation approach in which only a handful of data points are available to estimate two or more parameters. These empirical models could be improved by the use of structural models that assume reasonable economic behavior and provide a theoretical basis for cross-program analyses.     

Characterization of ballpoint pen inks by thermal and desorption and gas chromatography-mass spectrometry

The characterization of ink on paper is of importance for dating and comparing questioned ink entries in forensic document examination. Inks are commonly characterized by their colorant profile that is identified by well-established analytical methods. Numerous ink formulations show identical colorant profiles, though. In order to differentiate inks that are not distinguishable by colorant analysis, a method for the characterization of colorless ink ingredients, namely binders, solvents and additives is necessary. In this paper, we propose a technique for the analysis of colorless compounds in ballpoint inks using direct thermal desorption of the ink on paper followed by chemical analysis of the desorbed volatile compounds by gas chromatography-mass spectrometry. As compared to liquid extraction and subsequent analysis of the extracts, the technique avoids possible contamination risks. Sensitivity is very high due to the enrichment of volatile components by thermal desorption. Even from old samples, the chromatograms obtained by the method enable the determination of binder polymers, solvents and additives. Pure binders as used by ink manufacturers were analyzed for unambiguous assignment of analytical results to specific polymers. To prove the practical applicability, we analyzed 121 ballpoint pens, not all having the same colorant profile, and grouped the pens into resin and solvent categories.     

The search for the Russian Mafia

After the fall of communism in the Soviet Union, newspapers, films, academic researchers and intelligence agencies warned against a new phenomenon in organized crime: the “Russian mafiya.” In fact, since the fall of the Berlin Wall, Western European countries have noted an increase in the number of criminals from Central and Eastern Europe. In the mid-1990s, the Dutch judicial authorities made the issue a priority and established a special team to investigate crime with links to Eastern Europe. From 1999 to 2005, the KT NON crime team published several studies and reports on serious crime of this nature. In this paper, the authors, who were assigned to the KT NON crime team, discuss the findings of the two most recent publications on the “causes, nature, scale and threat of Central and Eastern European crime” and “mobile banditry,” respectively. They come to the conclusion that among other forms of crime, the phenomenon of mobile banditry seems to be the most serious manifestation of crime from post-communist countries. The Russian mafia was not found.