Abstract

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

Abstract
The full realization of the potential of a technology requires good understanding of its imple-mentation. During implementations, lack of compatibility between technology and its adopters require dynamic sequences of alignment. This process is understood to be central to the success in technology assimilation. This paper proposes a configurational model to explain and predict the alignment process during technology implementations, derived from a multiple case research of the implementation of a retinopathy screening program in networks of healthcare providers. It builds on and expands previous research capturing in a holistic way the alignment process and its nature of adaptation over time.

Abstract
The technological development of recent years has impacted the way companies, workers, and customers organize and interact with each other. Food retail stands out amongst the most affected sectors. New technologies, such as Artificial Intelligence (AI), lead to the emergence of a new retail concept, Smart Retailing, bringing benefits, not only for the retailer, but also for the consumer. In addition, they impact the jobs, in particular, store assistants' job. Despite the growing academic interest in these topics, the acceptance and impact of AI-based solutions on store assistants is scarcely studied. This work aims, therefore, to study the acceptance and perception of AI-based solutions by store assistants in food retail. Qualitative research was performed, having carried out 20 interviews with food retail store assistants that already work with AI-based solutions. Results show that store assistants are aware of what AI is and in which solutions it is used. They perceive these solutions as being beneficial for the performance of their duties, complementing their work instead of replacing them. They are willing to use these solutions and perceive them as being easy and intuitive to use. This study contributes with a starting point for future research on the topic. © 2023 IEEE.

Abstract
Supplier selection for strategic items requires a comprehensive framework dealing with qualitative and quantitative aspects of a company's competitive priorities and supply risk, decision scope, and uncertainty. In order to address these aspects, this study aims to tackle supplier selection for strategic items with a multi-sourcing, taking into account multi-criteria, incorporating uncertainty of decision-makers judgment and supplier-buyer parameters, and integrating with inventory management which the past studies have not addressed well. We develop a novel two-phase solution approach based on integrated multi-criteria decision -making (MCDM) and multi-objective simulation-optimization (S-O). First, MCDM methods, including fuzzy AHP and interval TOPSIS, are applied to calculate suppliers' scores, incorporating uncertain decision makers' judgment. S-O then combines the (quantitative) cost-related criteria and considers supply disruptions and uncertain supplier-buyer parameters. By running this approach on data generated based on previous studies, we evaluate the impact of the decision maker's and the objective's weight, which are considered important in supplier selection.

Abstract
This article addresses the Production Routing Problem (PRP), which consists of determining, in an integrated way, production and inventory planning, and vehicle routing to minimize the costs involved. In the problem, a plant is responsible for producing several types of products to meet the known demand of a set of customers using a homogeneous fleet of vehicles over the planning horizon. In the literature, evolutionary approaches have not been explored in depth for the PRP, specifically for the problem with multiple products. Thus, this work mitigates this gap, presenting a novel Memetic Algorithm and testing its effectiveness on randomly generated sets of instances, comparing the results obtained with a commercial optimization solver. In our solution approach, several classic operators from the literature were implemented. Furthermore, we propose four novel genetic operators. In addition, we evaluated the proposed method's performance in classical instances of literature considering a single item. The computational experiments were carried out to assess the impact of the numerous parameter combinations involving the metaheuristic, and, from statistical analyses, we evidence the proposed technique's robustness. Computational experiments showed that our proposed method outperforms the commercial solver Gurobi in determining feasibly high-quality solutions, mainly on large instances for the PRP with multiple items.