Search Results (5)

The increasing integration of artificial intelligence (AI) in decision-making processes has amplified discussions surrounding algorithmic authority—the perceived epistemic legitimacy of AI systems over human judgment. This study investigates how individuals attribute epistemic authority to AI, focusing on psychological, contextual, and sociotechnical factors. Existing research highlights the importance of trust in automation, perceived performance, and moral frameworks in shaping such attributions. Unlike prior conceptual or philosophical accounts of algorithmic authority, our study adopts a relational and empirically grounded perspective by operationalizing algority through psychometric measures and contextual assessments. To address knowledge gaps in the micro-level dynamics of this phenomenon, we conducted an empirical study using psychometric tools and scenario-based assessments. Here, we report key findings from a survey of 610 participants, revealing significant correlations between trust in automation (TiA), perceptions of automated performance (PAS), and the propensity to defer to AI, particularly in high-stakes scenarios like criminal justice and job-matching. Trust in automation emerged as a primary factor, while moral attitudes moderated deference in ethically sensitive contexts. Our findings highlight the practical relevance of transparency and explainability for supporting critical engagement with AI outputs and for informing the design of contextually appropriate decision support. This study contributes to understanding algorithmic authority as a multidimensional construct, offering empirically grounded insights for designing AI systems that are trustworthy and context-sensitive. Full article

The rapid advancement of artificial intelligence (AI) has introduced unprecedented opportunities and challenges, necessitating a robust ethical and regulatory framework to guide its development. This study reviews key ethical concerns such as algorithmic bias, transparency, accountability, and the tension between automation and human oversight. It discusses the concept of algor-ethics—a framework for embedding ethical considerations throughout the AI lifecycle—as an antidote to algocracy, where power is concentrated in those who control data and algorithms. The study also examines AI’s transformative potential in diverse sectors, including healthcare, Insurtech, environmental sustainability, and space exploration, underscoring the need for ethical alignment. Ultimately, it advocates for a global, transdisciplinary approach to AI governance that integrates legal, ethical, and technical perspectives, ensuring AI serves humanity while upholding democratic values and social justice. In the second part of the paper, the author offers a synoptic view of AI governance across six major jurisdictions—the United States, China, the European Union, Japan, Canada, and Brazil—highlighting their distinct regulatory approaches. While the EU’s AI Act as well as Japan’s and Canada’s frameworks prioritize fundamental rights and risk-based regulation, the US’s strategy leans towards fostering innovation with executive directives and sector-specific oversight. In contrast, China’s framework integrates AI governance with state-driven ideological imperatives, enforcing compliance with socialist core values, whereas Brazil’s framework is still lacking the institutional depth of the more mature ones mentioned above, despite its commitment to fairness and democratic oversight. Eventually, strategic and governance considerations that should help chief data/AI officers and AI managers are provided in order to successfully leverage the transformative potential of AI for value creation purposes, also in view of the emerging international standards in terms of AI. Full article

As the interest in social and assistive support robots (SASRs) grows, a review of 17 systematic reviews was conducted to assess their use in healthcare, emotional well-being, and therapy for diverse populations, including older adults, children, and individuals with autism and dementia. SASRs have demonstrated potential in alleviating depression, loneliness, anxiety, and stress, while also improving sleep and cognitive function. Despite these promising outcomes, challenges remain in identifying the most effective interventions, refining robot designs, and evaluating long-term impacts. User acceptance hinges on trustworthiness and empathy-driven design. Compared to earlier review studies, recent research emphasizes the ongoing significance of emotional engagement, the refinement of robot functionalities, and the need to address ethical issues such as privacy and autonomy through robust cybersecurity and data privacy measures. The field is gradually shifting towards a user-centered design approach, focusing on robots as tools to augment, rather than replace, human care. While SASRs offer substantial benefits for emotional well-being and therapeutic support, further research is crucial to enhance their effectiveness and address concerns about replacing human care. Algorethics (AI ethics), interdisciplinary collaboration, and standardization and training emerge as key priorities to ensure the responsible and sustainable deployment of SASRs in healthcare settings, reinforcing the importance of rigorous methodologies and ethical safeguards. Full article

The rapid advancement of artificial intelligence (AI) technology has catalyzed unprecedented innovation in the healthcare industry, transforming medical practices and patient care. However, this progress brings significant ethical challenges, highlighting the need for a comprehensive exploration of algorethics—the intersection of algorithm design and ethical considerations. This study aimed to conduct a narrative review of reviews in the field of algorethics with specific key questions. The review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar. The review revealed a growing emphasis on integrating fairness, transparency, and accountability into AI systems, alongside significant progress in ethical AI development. The importance of collaboration between different domains of scientific production, such as social sciences and standardization (like the IEEE), and the development of guidelines is significantly emphasized, with demonstrated direct impact in the health domain. However, gaps persist, particularly in the lack of standardized evaluation methods and the challenges posed by complex sectors like healthcare. The findings underscore the need and importance for robust data governance to prevent biases and highlight the importance of cross-disciplinary collaboration in creating comprehensive ethical frameworks for AI. The field of algorethics has important applications in the health domain, and there is a significant increase in attention, with a focus on addressing issues and seeking both practical and theoretical solutions. Future research should prioritize establishing standardized evaluation practices for AI, fostering interdisciplinary collaboration, developing sector-specific ethical guidelines, exploring AI’s long-term societal impacts, and enhancing ethical training for developers. Continued attention to emerging ethical standards is also crucial for aligning AI technologies with evolving ethical principles. Full article

Since 2018, a new terminology has been developed, called Algor-ethics, indicating the necessity for a dedicated study concerning the evaluation of an ethics applied to technology, to Algorithms and to Artificial Intelligence (AI). At the same time, since 1987, when the concept of sustainability was introduced, the discussion on this issue has become increasingly lively and has now spread to every area of life. In this paper, we would like to propose an application of the concept of sustainability to technological processes and in particular to the elaboration of AI systems. To reach this goal we will first try to build an ethical framework, here called Dynamical Techno-Algor-Ethical Composition, to define the interaction between the most important ethical ingredients involving the human person in relation to technology, taking a person-centered approach. Out of this will emerge a possible structure and definition of Technological Sustainability. The second step will consist of evaluating the process for the elaboration of an AI algorithm as a concrete application of the previously analyzed framework, to set ethical contents composing what we might call a good and sustainable algorithm. Full article