The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
This study investigates the efficiency paradox of AI, emphasizing the impact of technical optimization on human values, social cohesiveness, and workplace culture and indicates that the ethical and human-centric integration of AI is crucial to ensure that efficiency gains align with inclusivity, justice, and the maintenance of social well-being in contemporary workplaces.
Nandini Aquila Lukito, Nazwa Sabila· International Journal Admini...· 0 citations
An integrated conceptual and heuristic framework centred on Silent Cognitive Erosion and Cognitive Sustainability is developed, arguing that the future of AI governance depends not only on the capabilities of intelligent systems, but also on the capacity of societies to sustain the cognitive, informational, educational, institutional, and governance conditions necessary for meaningful human participation and adaptive oversight across generations.
Preliminary empirical support for ethical vigilance as a relevant construct in AI governance and organizational decision-making is provided and useful insights are provided into how AI systems can be designed to be explainable, accountable and ethically sound to counter rising organizational risks.
Avinash Chopra· VINE Journal of Information...· 0 citations
The accelerated adoption of artificial intelligence (AI) and digital technologies is transforming both society and ecosystems. While digitalization is often promoted as a tool for sustainability, critics point to its significant environmental footprint, including energy-intensive data centers, rare resource extraction, and electronic waste. Noreen Herzfeld (2025) emphasizes that AI and digital infrastructures are not ethically neutral: their design, deployment, and scale have profound ecological consequences that require careful scrutiny. This paper explores how ecotheology can serve as a critical lens for analyzing these developments. Drawing on Herzfeld’s insights and relevant papal documents – Laudato si’ (2015), Antiqua et nova (2025), and Pope Francis’s 2024 World Day of Peace message – the paper argues that ethical and spiritual reflection is essential for evaluating the ecological implications of AI and digitalization. Laudato si’ provides a framework of integral ecology, emphasizing the interconnectedness of technological progress, environmental responsibility, and human well-being, while Antiqua et nova highlights AI’s dual potential: supporting sustainability efforts but also imposing significant environmental costs. Guided by this framework, the paper addresses the fol-lowing research questions: How can ecotheology provide an ethical frame-work for evaluating the ecological impacts of AI and digital technologies? In what ways can moral and spiritual reflection guide sustainable AI practices while minimizing environmental costs? How can ecotheological principles, such as those highlighted in Laudato si’, inform responsible design, deployment, and governance of digital infrastructures? How can a relational and ethical perspective help move beyond polarized narratives that present digitalization as either destructive or salvific for the planet? Finally, what normative criteria and practical guidelines can ecotheology offer to align techno-logical innovation with care for creation and human well-being?
By integrating ecotheological critique with technological analysis, the paper moves beyond simplistic dichotomies toward a nuanced understanding that considers relational, ethical, and ecological responsibilities. Ecotheology offers normative criteria and reflective tools to guide sustainable AI practices, fostering alignment between technological innovation and care for creation. Ultimately, this approach underscores the indispensable role of ethical discernment in shaping a digital future that genuinely supports ecological sustainability.
Nadja Furlan Štante· Programme and abstracts· 0 citations
Artificial intelligence is one of the main drivers of contemporary technological transformation, with effects that transcend the realm of innovation and directly impact the environment and the world of work. Its expansion is underpinned by supply chains for the extraction of critical mineral resources, energy-intensive consumption, and forms of labor organization mediated by digital platforms, all of which generate significant socio-environmental impacts. An analysis of this process reveals that, while promoting technical advances, artificial intelligence intensifies pressure on fragile ecosystems and entrenches new forms of precariousness. The study’s overall objective is to analyze the hidden costs of artificial intelligence, linking environmental effects to forms of labor precariousness in the Global South. As specific objectives, it seeks to identify the impacts of mineral extraction and energy consumption on sustainability, examine the mechanisms of precariousness and the invisibilization of digital labor, discuss global inequalities resulting from the data economy, and evaluate alternatives for regulation and governance that integrate environmental and social justice. The research question guiding this study is: In what ways does the expansion of artificial intelligence contribute to exacerbating the environmental crisis and deepening labor precariousness in the Global South? The methodology adopted is qualitative in nature and is based on a critical literature review, which made it possible to articulate different analytical dimensions and interpret contradictions present in discourses on technological neutrality. The results demonstrate that artificial intelligence, when examined from a broad perspective, reveals environmental and social costs that cannot be ignored. The conclusion indicates that any innovation agenda must incorporate robust sustainability parameters and guarantees of decent work, lest it reinforce historical asymmetries and exacerbate socio-environmental vulnerabilities.
Rodrigo Oliveira Miranda, João Pedro Donato Veloso, Sabrina Pâmela Matos da Silva Duzino et al.· Revista de Estudos Interdisc...· 0 citations
The declared benefits for people, communities and the planet of sharing economy (SE) have been praised for years, even though studies began to highlight that SE had failed to meet certain expectations. Although substantial research focuses on the SE impacts, literature examining its adverse social effects needs to be more extensive. This research aims to investigate the SE's social drawbacks from various angles, considering different standpoints, such as providers, consumers, companies and communities.
By conducting a systematic literature review of 103 academic papers, the study provides a comprehensive description and analysis of the SE's unintended social effects at both micro- and macro-levels; the negative social effects have been compared with the SE promises, creating a bridge between the main claims and SE actual impact.
While the expansion of the SE has contributed to market destabilization and social externalities, the literature suggests that these issues stem from specific implementation models and regulatory gaps rather than the sharing logic itself. Company narratives reinforce expectations that often go unmet, widening the gap between promises and reality. This research identifies future research directions, managerial implications and solutions to mitigate negative social consequences.
The originality of this study lies in the analysis of the negative social effects from multiple standpoints and their comparison with the SE promises, creating a bridge between the main claims, SE actual impact and the proposed solutions mentioned in the literature. These findings demonstrate how negative impacts have contradicted SE’ promises.
Martina Toni, F. Corsini, G. Mattia· Journal of service theory an...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
