Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and two focus groups, (...) the goal of the current study is to understand how companies approach ethical issues related to AI systems. A structured analysis of the transcripts brought out many actual practices and findings, which are presented around the following main research topics: ethics and principles, privacy, explainability, and fairness. The interviewees also raised issues of accountability and governance. Finally, some recommendations are suggested such as developing specific sector regulations, fostering a data-driven organisational culture, considering the algorithm’s complete life cycle, developing and using a specific code of ethics, and providing specific training on ethical issues. Despite some obvious limitations, such as the type and number of companies interviewed, this work identifies real examples and direct priorities to advance the research exploring the gap between practice and principles in AI ethics, with a specific focus on Spanish companies. (shrink)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out an (...) epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents. (shrink)

The purpose is to provide researchers with reliable Scientific Research Data from the massive amounts of research data to establish a sustainable Scientific Research environment. Specifically, the present work proposes establishing an Intelligent Recommendation System based on Machine Learning algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering ] is established to improve the utilization efficiency of SRD and Sustainable Development of (...) SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system’s feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82–93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency. (shrink)

Who should decide how a machine will decide what to do when it is driving a car, performing a medical procedure, or, more generally, when it is facing any kind of morally laden decision? More and more, machines are making complex decisions with a considerable level of autonomy. We should be much more preoccupied by this problem than we currently are. After a series of preliminary remarks, this paper will go over four possible answers to the question raised above. First, (...) we may claim that it is the maker of a machine that gets to decide how it will behave in morally laden scenarios. Second, we may claim that the users of a machine should decide. Third, that decision may have to be made collectively or, fourth, by other machines built for this special purpose. The paper argues that each of these approaches suffers from its own shortcomings, and it concludes by showing, among other things, which approaches should be emphasized for different types of machines, situations, and/or morally laden decisions. (shrink)

The combined effect of technological innovations in the workplace and the lockdowns imposed by the COVID-19 pandemic has rapidly increased the prominence of remote working, with an undeniable impact on both business and society. In light of this organizational and sociological change, this article analyzes how this renewed work environment can be the place where workers can develop several relevant virtues, specifically moderation, integrity, and mercy. This new environment may also present the opportunity to develop a number of opposing vices, (...) which are also explained and analyzed. The article concludes by suggesting some implications for managers who wish to promote virtuous behaviors in the new context of remote work. (shrink)