Suchergebnisse

This thesis investigates human-algorithm interactions in sociotechnological ecosystems. Specifically, it applies machine learning and statistical methods to uncover political dimensions of algorithmic influence in social media platforms and automated decision making systems. Based on the results, the study discusses the legal, political and ethical consequences of algorithmic implementations. ; Diese Arbeit untersucht Mensch-Algorithmen-Interaktionen in sozio-technologischen Ökosystemen. Sie wendet maschinelles Lernen und statistische Methoden an, um politische Dimensionen des algorithmischen Einflusses auf Socialen Medien und automatisierten Entscheidungssystemen aufzudecken. Aufgrund der Ergebnisse diskutiert die Studie die rechtlichen, politischen und ethischen Konsequenzen von algorithmischen Anwendungen.

PurposeThe crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.Design/methodology/approachA feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.FindingsThis study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.Practical implicationsThe managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.Originality/valueThis study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

The paper proposes a new approach to measure inflation expectations of the Russian population based on text mining of information on the Internet with the help of machine learning techniques. Two indicators were constructed on the base of readers' comments to inflation news in major Russian economic media available in the web at the period from 2014 through 2016: with the help of words frequency and sentiment analysis of comments content. During the whole considered period of time both indicators were characterized by dynamics adequate to the development of macroeconomic situation and were also able to forecast dynamics of official Bank of Russia indicators of population inflation expectations for approximately one month in advance.

Advancements in materials discovery tend to rely disproportionately on happenstance and luck rather than employing a systematic approach. Recently, advances in computational power have allowed researchers to build computer models to predict the material properties of any chemical formula. From energy minimization techniques to machine learning-based models, these algorithms have unique strengths and weaknesses. However, a computational model is only as good as its accuracy when compared to real-world measurements. In this work, we take two recommendations from a thermoelectric machine learning model, TaVO[Formula: see text] and GdTaO[Formula: see text], and measure their thermoelectric properties of Seebeck coefficient, thermal conductivity, and electrical conductivity. We see that the predictions are mixed; thermal conductivities are correctly predicted, while electrical conductivities and Seebeck coefficients are not. Furthermore, we explore TaVO[Formula: see text]'s unusually low thermal conductivity of 1.2 Wm[Formula: see text]K[Formula: see text], and we discover a possible new avenue of research of a low thermal conductivity oxide family.

AbstractIn the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the 'black box' and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London's position. In particular, we make two claims. First, we claim that London's solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London's views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like.

Background and purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications. Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice. Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

This paper investigates how institutional pressures affect the development of Circular Economy (CE) in firms. Using Institutional Entrepreneurship as a theoretical framework, this paper considers three different levels of institutional pressures (coercive, normative, and mimetic) to examine the effect of each pressure and their interactions on the development of CE. Seeking to clarify the debate on the effect of institutional pressures, this paper considers that the main limitation arises from the fact that previous research has analysed the relationship between institutional pressures without considering the interaction between them and the non-linearity of the processes. Deviating from previous papers, our analysis combines regression methods with Machine learning (i.e. Artificial Neural Networks), and employs data from the EU survey on Public Consultation on the Circular Economy. This research finds that while coercive pressures have a compulsory effect on the development of CE, mimetic and normative pressures do not have an effect by themselves, but only in interaction with coercive pressures. Moreover, this paper shows that the application of machine learning tools has an important contribution in solving interaction problems. From the perspective of environmental policy, this means that a comprehensive policy is required, which implies the coexistence or interaction of the three types of pressures.

This paper investigates how institutional pressures affect the development of Circular Economy (CE) in firms. Using Institutional Entrepreneurship as a theoretical framework, this paper considers three different levels of institutional pressures (coercive, normative, and mimetic) to examine the effect of each pressure and their interactions on the development of CE. Seeking to clarify the debate on the effect of institutional pressures, this paper considers that the main limitation arises from the fact that previous research has analysed the relationship between institutional pressures without considering the interaction between them and the non-linearity of the processes. Deviating from previous papers, our analysis combines regression methods with Machine learning (i.e. Artificial Neural Networks), and employs data from the EU survey on Public Consultation on the Circular Economy. This research finds that while coercive pressures have a compulsory effect on the development of CE, mimetic and normative pressures do not have an effect by themselves, but only in interaction with coercive pressures. Moreover, this paper shows that the application of machine learning tools has an important contribution in solving interaction problems. From the perspective of environmental policy, this means that a comprehensive policy is required, which implies the coexistence or interaction of the three types of pressures.