Anthropological Singularity and Algorithmic Stratification: A Medical-Sociological Reframing of the Bioethical Principles Governing the Use of Artificial Intelligence in Healthcare
The integration of artificial intelligence (AI) into medical practice is a paradigmatic transformation that compels a reconsideration of the very conceptual structure of bioethics. We argue that this transformation is the local symptom of a wider mutation we designate the anthropological singularity: the threshold at which humanity withdraws from natural evolution and enters an artificial, self-directed one, along three converging vectors—the virtualisation of social space, the cognitive and relational reconfiguration of the human agent through interaction with AI, and the modification of the biological substratum through synthetic biology and gene editing. This approach must be seen as a conceptual study, and not as a systematic literature review, since the study does not aggregate empirical findings but constructs them by defending a theoretical framework and drawing on medical sociology, medical anthropology and biolaw, in order to examine the recurrent tensions between transparency vs. performance, justice vs. efficacy, precaution vs. innovation. We argue that algorithmic bias is part of socio-technical ecosystems, being a systemic property of these. We also argue that responsibility must migrate towards distributed frameworks between human and non-human agents, and that the digital revolution could produce an algorithmic stratification of populations, now prolonged through gene editing into the very biology of future generations. An augmented bioethics is required—not mere delegation of moral judgement to algorithms, but a reflexive framework that revises the ontological presuppositions of principlism, sustained by multi-stakeholder governance and deliberate public policies.
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on experienced agile teams and organizations, connecting better to existing streams of research in more established fields, giving more attention to management-oriented approaches, and finally give more emphasis to the core ideas in agile software development in order to increase our understanding. We hope that this preliminary roadmap serves as a starting point for creating a common research agenda and enables the generation of fruitful discussions and research results from the field.
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 92 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
MIT News · Artificial Intelligence· news.mit.eduSep 11, 2026
The handheld catheterization device AI-GUIDE, created by Lincoln Laboratory and Massachusetts General Hospital, promises improved health outcomes for injured service members and civilians.
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