Computing and Experiments

The question about the scientific nature of computing has been widely debated with no universal consensus reached about its disciplinary status. Positions vary from acknowledging computing as the science of computers to defining it as a synthetic engineering discipline. In this paper, we aim at discussing the nature of computing from a methodological perspective. We consider, in particular, the nature and role of experiments in this field, whether they can be considered close to the traditional experimental scientific method or, instead, they possess peculiar and unique features. We argue that this experimental perspective should be taken into account when discussing the status of computing. We critically survey how the experimental method has been conceived and applied in computing, and some open issues that could be tackled with the aid of the history of science, the philosophy of science, and the philosophy of technology

Rethinking Experiments in a Socio-Technical Perspective: The Case of Software Engineering

Experiments in computing share many characteristics with the traditional experimental method, but also present significant differences from a practical perspective, due to their aim at producing software artifacts and the central role played by human actors and organizations (e.g., programmers, project teams, software houses) involved in the software development process. By analyzing some of the most significant experiments in the subfield of software engineering, we aim at showing how the conceptual framework that supports experimental methodology in this context needs an extension in a socio-technical perspective

Qualification and Quantification of Fairness for Sustainable Mobility Policies

The adoption of new mobility technologies on a large-scale plays a crucial role to promote a green transition in the mobility field. Nonetheless, the acceptance of new mobility solutions implies radical changes in the everyday lives of individuals and, thus, it can be hampered by many different factors besides transport habits, such as socio-economic individual features. For this reason, it is essential to design human-centered policies directly addressing such barriers, avoiding the unwanted effect of amplifying inequalities at the edges of society. To this end, we propose a data-driven approach to embed socio-economic factors in the design of new mobility strategies that quantitatively account for fairness in a control-oriented and dynamic fashion. The formalization (and the inclusion in the approach) of the concepts of doxastic equality and equity allows us to mitigate epistemic exclusions, assessing system fairness. Thus, by combining tools from the control framework with those of philosophy, our approach offers an actionable tool for the support of the design of fair policies to foster the adoption of sustainable mobility habits.</p

Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity

Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness

AU :Wearable devices arePleaseconfirmthatallheadinglevelsarerepresentedcorrectly:increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity

RoCKIn Benchmarking and Scoring System

The main innovation brought forth by the European Project RoCKIn is the definition, implementation and application to an actual robot competition of the novel paradigm of benchmarking through competitions. By doing so, RoCKIn set in motion an evolutionary process to transform robot competitions from successful showcases with limited scientific impact into benchmarking tools for the consistent and objective evaluation of the performance of autonomous robot systems. Our work began by revisiting, in the light of the features and limitations of a competition setting, the very foundations of the scientific method; then we built on these by designing a novel type of competitions where the concepts of benchmark and objective performance metrics are the key points; finally, we arrived to the implementation of such concepts in the form of a real-world robot competition. This chapter describes the above process, explaining how each of its several aspects (theoretical, technical, procedural) has been tackled by RoCKIn. Special attention will be devoted to the problems of defining performance metrics and of capturing the ground truth needed to reliably assess robot perceptions and actions