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Riccardo Camilli, Alessandro Mechelli, Alessandra Stefanoni and Fabrizio Rossi
To date, the studies on managerial loss aversion have produced contradictory findings, making it impossible to: (i) identify the ultimate impact of managerial loss aversion on the value that organisations create for themselves and for their stakeholders,...
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Ulrich Aïvodji, François Bidet, Sébastien Gambs, Rosin Claude Ngueveu and Alain Tapp
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial train...
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Norah Alshareef, Xiaohong Yuan, Kaushik Roy and Mustafa Atay
In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demogra...
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Stefan Strauß
The increasing use of AI in different societal contexts intensified the debate on risks, ethical problems and bias. Accordingly, promising research activities focus on debiasing to strengthen fairness, accountability and transparency in machine learning....
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Arno Nuijten, Nick Benschop, Antoinette Rijsenbilt and Kristinka Wilmink
Decisions by small and medium enterprise (SME) entrepreneurs are plagued by a variety of cognitive biases. Extant literature has mainly focused on a limited number of important biases (e.g., overconfidence) in a handful of important entrepreneurial decis...
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Milo? Lompar, Branislava Lalic, Ljiljana Dekic and Mina Petric
Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. T...
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Sangkyun Lee and Jeonghyun Lee
Deep neural networks (DNNs) have been quite successful in solving many complex learning problems. However, DNNs tend to have a large number of learning parameters, leading to a large memory and computation requirement. In this paper, we propose a model c...
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