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Algorithmic decision‐making (ADM) systems have come to support, pre‐empt or substitute for human decisions in manifold areas, with potentially significant impacts on individuals' lives. Achieving transparency and accountability has been formulated as a general goal regarding the use of these systems. However, concrete applications differ widely in the degree of risk and the accountability problems they entail for data subjects. The present paper addresses this variation and presents a framework that differentiates regulatory requirements for a range of ADM system uses. It draws on agency theory to conceptualize accountability challenges from the point of view of data subjects with the purpose to systematize instruments for safeguarding algorithmic accountability. The paper furthermore shows how such instruments can be matched to applications of ADM based on a risk matrix. The resulting comprehensive framework can guide the evaluation of ADM systems and the choice of suitable regulatory provisions.

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Since the h-index has been invented, it is the most frequently discussed bibliometric value and one of the most commonly used metrics to quantify a researcher's scientific output. The more it is increasingly gaining popularity to use the metric as an indication of the quality of a job applicant or an employee the more important it is to assure its correctitude. Many platforms offer the h-index of a scientist as a service, sometimes without the explicit knowledge of the respective person. In this article we show that looking up the h-index for a researcher on the five most commonly used platforms, namely AMiner, Google Scholar, ResearchGate, Scopus and Web of Science, results in a variance that is in many cases as large as the average value. This is due to the varying definitions of what a scientific article is, the underlying data basis, and different qualities of the entity recognition problem. To perform our study, we crawled the h-index of the worlds top researchers according to two different rankings, all the Nobel Prize laureates except Literature and Peace, and the teaching staff of the computer science department of the TU Kaiserslautern Germany with whom we additionally computed their h-index manually. Thus we showed that the individual h-indices differ to an alarming extent between the platforms. We observed that researchers with an extraordinary high h-index and researchers with an index appropriate to the scientific career path and the respective scientific field are affected alike by these problems.

In this paper we present the results of the project "#Datenspende" where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign. Analyzing the donated result lists we prove, that the room for personalization of the search results is very small. Thus the opportunity for the effect mentioned in Eli Pariser's filter bubble theory to occur in this data is also very small, to a degree that it is negligible. We achieved these results by applying various similarity measures to the result lists that were donated. The first approach using the number of common results as a similarity measure showed that the space for personalization is less than two results out of ten on average when searching for persons and at most four regarding the search for parties. Application of other, more specific measures show that the space is indeed smaller, so that the presence of filter bubbles is not evident. Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine's degree of personalization for any desired search terms. The general design can also be transferred to intermediaries, if appropriate APIs restrict selective access to contents relevant to the study in order to establish a similar degree of trustworthiness.