Algorithmic Cities: The Sociological Impacts of Artificial Intelligence on Urban Life

Artificial Intelligence (AI) is rapidly inducing revolutionary transformations in entire aspects of life. Integrating AI into the educational landscape is heralding a new era of pedagogical innovations and redefining learning experiences. As AI technologies continuously evolve, the integration of AI-driven systems in the instructional settings is an inevitable progression and a formidable opportunity. This study is a scholarly attempt to explore the multi-faceted implications of AI integration in the spectrum of education highlighting its potential to revamp the traditional models. Thorough analysis of extant literature reveals that AI-driven platforms facilitate personalized learning experience which can accommodate diverse needs and learning paces of individual students. In addition to this, AI technologies have the potential to revolutionize assessment techniques. As the conventional assessment models fail to capture the true reflections of learning progress, AI assisted models can track, measure, analyze the academic output and provide real-time feedback which is transparent and free form bias. Moreover, AI-powered models have streamlined the administrative tasks as well. By automating routine jobs, optimizing resource allocation, and improving decision making, AI is minimizing human effort and maximizing productivity. However, AI integration poses some challenges and drawbacks. The concerns like data privacy and security, inequality, accessibility, algorithm bias, dehumanization, over-reliance on AI technologies and other ethical considerations are also looming. This study also presents certain future directions and policy perspectives on how to overcome these challenges and build an educational landscape which has the potential to fully utilize the AI-driven systems for academic achievement and learning progress of the students. As AI is revolutionizing entire spheres of life including education, it is imperative to critically engage with both opportunities and challenges posed by AI ensuring the principles of fairness, inclusivity, and integrity.

Unsafe drinking water is a global concern that poses serious risks to public health, especially in developing nations, where tainted water can spread diseases such as cholera, polio, and diarrhea, which can result in several health issues and deaths. Children and immunocompromised people are the most vulnerable groups that suffer disproportionately from waterborne illnesses. Promising approaches to reduce the burden of waterborne diseases and revolutionize drinking water management are provided by artificial intelligence (AI). Public health authorities and water industries can improve safe drinking water distribution, treatment, and monitoring using AI-powered models and approaches. AI enables predictive modeling to support sustainable water management techniques, maximize resource usage, and identify problems with infrastructure and water quality earlier. AI, coupled with Geographic Information Systems (GIS) and machine learning (ML) models such as random forest classifiers, aids in cholera risk prediction and enhances waterborne disease detection. Advanced AI models facilitate drought forecasting, reservoir optimization, and real-time water monitoring, improving water management and resource conservation. AI-driven systems, including predictive analytics and intelligent water distribution models, show potential for enhancing water safety, mitigating risks, and promoting sustainable water practices. However, several challenges must be overcome when incorporating AI into water management, such as concerns about data quality, infrastructure constraints, and ethical difficulties. Genetic sequencing and metagenomic analyses, which provide insights into microbial dynamics and water quality maintenance, are potential future areas in AI applications for water management. A balanced approach prioritizing equitable deployment, infrastructure readiness, workforce development, robust governance, collaborative efforts, ethical standards, and transparent regulatory frameworks, ensuring social equity and economic efficiency with current norms and policies, is required for AI integration to address diseases attributable to unsafe drinking water. These AI models are expedient to fully optimize WASH disease management to increase access to clean water, reduce the incidence of waterborne diseases, and advance global health.

Artificial intelligence (AI) is playing an increasingly vital role in transforming the medical field, particularly in areas like medical imaging, clinical decision-making, pathology, and minimally invasive surgery. The rapid growth of medical data and the continuous refinement of machine learning algorithms have propelled AI's integration into healthcare. This study explores the advancements and applications of AI, specifically machine learning algorithms and intelligent medical robots, in enhancing diagnostics, treatment, and healthcare delivery. A comprehensive review of current AI applications in healthcare, including its use in medical imaging, pathology, clinical decision-making, and robotic-assisted surgery, was conducted. AI technologies such as the Da Vinci Surgical Robot and machine learning-based diagnostic tools have significantly improved diagnostic accuracy and the precision of minimally invasive surgeries. AI-driven systems also contributed to better clinical decision support, faster recovery times for patients, and more accurate treatment plans. Overall, AI, through machine learning algorithms and intelligent medical robots, is revolutionizing healthcare by offering promising improvements in diagnostics, surgical precision, and patient care.

We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent developments in AI are the result of increased processing power, improvements in algorithms and the exponential growth in the volume and variety of digital data. Many applications of AI have started entering into our every-day lives, from machine translations, to image recognition, and music generation, and are increasingly deployed in industry, government, and commerce. Connected and autonomous vehicles, and AI-supported medical diagnostics are areas of application that will soon be commonplace. There is strong global competition on AI among the US, China, and Europe. The US leads for now but China is catching up fast and aims to lead by 2030. For the EU, it is not so much a question of winning or losing a race but of finding the way of embracing the opportunities offered by AI in a way that is human-centred, ethical, secure, and true to our core values. The EU Member States and the European Commission are developing coordinated national and European strategies, recognising that only together we can succeed. We can build on our areas of strength including excellent research, leadership in some industrial sectors like automotive and robotics, a solid legal and regulatory framework, and very rich cultural diversity also at regional and sub-regional levels. It is generally recognised that AI can flourish only if supported by a robust computing infrastructure and good quality data: • With respect to computing, we identified a window of opportunity for Europe to invest in the emerging new paradigm of computing distributed towards the edges of the network, in addition to centralised facilities. This will support also the future deployment of 5G and the Internet of Things. • With respect to data, we argue in favour of learning from successful Internet companies, opening access to data and developing interactivity with the users rather than just broadcasting data. In this way, we can develop ecosystems of public administrations, firms, and civil society enriching the data to make it fit for AI applications responding to European needs. We should embrace the opportunities afforded by AI but not uncritically. The black box characteristics of most leading AI techniques make them opaque even to specialists. AI systems are currently limited to narrow and well-defined tasks, and their technologies inherit imperfections from their human creators, such as the well-recognised bias effect present in data. We should challenge the shortcomings of AI and work towards strong evaluation strategies, transparent and reliable systems, and good human-AI interactions. Ethical and secure-by-design algorithms are crucial to build trust in this disruptive technology, but we also need a broader engagement of civil society on the values to be embedded in AI and the directions for future development. This social engagement should be part of the effort to strengthen our resilience at all levels from local, to national and European, across institutions, industry and civil society. Developing local ecosystems of skills, computing, data, and applications can foster the engagement of local communities, respond to their needs, harness local creativity and knowledge, and build a human-centred, diverse, and socially driven AI. We still know very little about how AI will impact the way we think, make decisions, relate to each other, and how it will affect our jobs. This uncertainty can be a source of concern but is also a sign of opportunity. The future is not yet written. We can shape it based on our collective vision of what future we would like to have. But we need to act together and act fast.

Despite advancements in anesthesia techniques and equipment, anesthesia-related complications continue to occur, often due to human errors and the limitations of current tools. The integration of artificial intelligence (AI) into anesthesia presents a transformative opportunity to enhance patient safety and improve outcomes throughout the perioperative journey. By leveraging machine learning (ML) and deep learning (DL), AI can analyze vast datasets to detect subtle patterns, predict risks such as difficult intubation or hemodynamic instability, and enable more proactive management. Furthermore, AI-driven systems have the potential to optimize anesthetic control, reducing variability and enhancing precision. In the postoperative phase, AI can improve personalized pain management and monitoring, further enhancing recovery and patient satisfaction. However, challenges such as data privacy concerns, lack of opacity, and the potential erosion of human interaction in care must be carefully addressed. Ultimately, the future of anesthesiology lies in a synergistic relationship between AI and human expertise – where AI amplifies precision and foresight, while anesthesiologists maintain the empathy and clinical judgment needed to navigate complex patient care.

This chapter first provides a section on artificial intelligence (AI) in high risk systems, giving an overview over the current progress in standards relating to this topic. Next, a section addresses explainable AI (XAI) both as a technical concept and as a concept that has evident human and organizational sides to it. Lastly, a section on the concept of safety of intended functionality (SOTIF) is provided as it addresses safety in AI-driven systems, especially autonomous vehicles. This approach helps mitigate risks from functional insufficiencies in AI algorithms, making it vital for deploying AI safely in high risk areas.

Despite the growing interest in Artificial Intelligence (AI) for pediatric rehabilitation, family engagement in the technologies design remains limited. Understanding how AI-driven tools align with family needs, caregiving routines, and ethical concerns is crucial for their successful adoption. In this study, we actively involved nine families of children with Cerebral Palsy (CP) in an online participatory design workshop, underscoring both the feasibility and the need of integrating family’s perspectives into AI development. Families enthusiastically participated, not only sharing insights but also appreciating the opportunity to contribute to shaping future technologies. Their active engagement challenges the assumption that co-design with families is complex or impractical, highlighting how structured yet flexible methodologies can make such crucial initiatives highly effective. The online format further facilitated participation, allowing families to join the discussion and ensuring a diverse range of perspectives. The workshop’s key findings reveal three core priorities for families: 1. AI should adapt to daily caregiving routines rather than impose rigid structures; 2. digital tools should enhance communication and collaboration between families and clinicians, rather than replace human interaction; and 3. AI-driven systems could empower children’s autonomy while maintaining parental oversight. Additionally, families raised critical concerns about data privacy, transparency, and the need to preserve empathy in AI-mediated care. Our findings reinforce the urgent need to shift toward family-centered AI design, moving beyond purely technological solutions toward ethically responsible, inclusive innovations. This research not only demonstrates the possibility and success of engaging families in co-design processes but also provides a model for future AI development that genuinely reflects the lived experiences of children and caregivers.

The integration of Artificial Intelligence (AI) in credit scoring systems marks a pivotal moment in financial inclusion, offering opportunities to address systemic inequities while presenting challenges in ensuring fairness. Traditional credit evaluation methods have historically marginalized underserved communities, perpetuating cycles of exclusion and economic disparity. AI-driven systems, when designed with Diversity, Equity, and Inclusion [DEI] principles, provide a pathway to transform these dynamics by promoting credit equity and expanding financial access. This paper explores the dual role of AI in modern credit scoring systems: as both a tool for perpetuating bias and a solution for mitigating it. It begins with an overview of how AI impacts underserved communities, analysing how algorithmic decision-making can inadvertently amplify discrimination if not carefully monitored. The discussion then focuses on strategies for embedding DEI principles into algorithm design, emphasizing the need for transparency, accountability, and the inclusion of diverse perspectives in AI development. These strategies are critical for identifying and correcting bias, ensuring that AI serves as a force for equity rather than exclusion. Additionally, the paper examines the use of non-traditional credit data, such as rental histories, utility payments, and employment records, as a means of bridging gaps in financial access for minorities. By expanding the criteria for creditworthiness, these alternative data sources challenge conventional models that often disadvantage marginalized populations. The paper concludes by highlighting the broader societal and ethical implications of integrating DEI into AI-driven financial systems, urging stakeholders to adopt inclusive practices that balance innovation with fairness.

Recent advancements in artificial intelligence (AI) and augmented reality (AR) hold significant promise for revolutionizing the management of neurological diseases. These technologies enable enhanced diagnostic accuracy, personalized treatment plans, and innovative rehabilitation strategies. AI-driven systems can analyze complex neuroimaging data, track disease progression, and assist in neuroprosthetic interventions, while AR improves surgical precision and supports interactive, immersive training for healthcare professionals. This narrative review explores the current and potential applications of AI and AR in neurology, focusing on their impact on diagnosing and treating neurodegenerative disorders, stroke, and other neurological conditions. We also address the challenges of data security, bias in AI models, and the high cost of AR devices, along with the ethical implications of integrating these technologies into clinical practice. The review concludes with a discussion of future directions, emphasizing the need for larger, long-term studies, interdisciplinary collaborations, and policy frameworks to ensure the safe, equitable, and effective implementation of AI and AR in neurological care.

The integration of artificial intelligence (AI) in local governments has gained increasing attention as a means to enhance civic engagement and streamline public participation. This study examines how local governments utilize AI-powered platforms for civic engagement, exploring the perspectives of public servants and developers on the implementation of AI, its benefits, challenges, and ethical considerations. With a qualitative approach, semi-structured interviews were conducted with developers, product designers, public servants, and decision-makers involved in developing and using the AI-powered platform for civic engagement initiatives. Thematic analysis was employed to identify key themes related to AI functionalities, user experiences, and governance challenges. The preliminary results reveal that four distinct types of AI applications have been employed, including AI-powered optical character recognition (OCR), AI-based content moderation, AI sensemaking, and AI-driven translation. These AI tools facilitate efficiency in data processing, enhance accessibility, and support decision-making. However, concerns regarding AI transparency, data privacy, and public trust remain significantly challenging. Additionally, public servants emphasized the need for AI literacy training and the development of ethical guidelines to ensure the responsible use of AI in local governance. This study contributes to the growing discourse on AI in civic engagement by offering insights into the practical and ethical dimensions of AI adoption in local governments. The findings underscore the need for policy frameworks, digital inclusivity measures, and ongoing capacity-building efforts to enhance AI-driven public participation.

The abstract presents a research study focusing on real-time license plate verification, a key feature of electronic systems that operate by rapidly identifying and removing identification numbers from vehicle registration in a dynamic global environment. The research leverages the combination of artificial intelligence (AI) and machine learning (ML) techniques, specifically the integration of region-based convolutional neural networks (RCNN) and advanced RCNN algorithms, to create a powerful and readily available system. In terms of methods, this research optimizes algorithm performance and deploys the system in a cloud-based environment to improve accessibility and scalability. Through careful design and optimization, the proposed system has achieved a consistent result in license recognition, as evident from the well-accounted evaluation of performance, including precision, recall, and computational efficiency. The results demonstrate the efficiency and usability of this system in a real installation and promise to revolutionize automatic vehicle identification. Finally, the integration of artificial intelligence and machine learning technology into real-time license plate recognition signifies changes in traffic management, assessment safety and smart city plans. Therefore, interdisciplinary collaboration and continuous innovation are crucial to shaping a sustainable and balanced future for intelligent transportation systems.

PurposeDespite the increasing trend of integrating artificial intelligence (AI) into organizational workforces, there remains a lack of understanding regarding how employee–AI interaction shapes employee service performance. The objective of this study is to investigate the impacts of employee–AI interaction fluency on in-role and out-of-role employee service performance in different delivery service (smart helmet services vs in-vehicle AI services) contexts.Design/methodology/approachDrawing on mind perception theory and self-efficacy theory, a research model that reflected the impacts of employee–AI interaction fluency on employee service performance was developed. This research model was then tested through two empirical studies using data collected from frontline employees in the delivery service industry.FindingsThe present study found that employee–AI interaction fluency acts both directly on employee service performance as well as indirectly via mind perceptions. Specifically, in the smart helmet service context, employee–AI interaction fluency affects in-role employee service performance via both perceived empathy and competence. By contrast, this impact shifts to out-of-role employee service performance in the in-vehicle AI service context, where perceived competence fully mediates the relationship between interaction fluency and service performance. In addition, the impact of employee–AI interaction fluency on perceived competence will be diminished among frontline employees with high levels of self-efficacy.Practical implicationsThis study offers valuable insights for AI device developers and managers aiming to improve both in-role and extra-role employee service performance through the lens of employee–AI interactions. In contexts with a high degree of employee–AI collaboration, such as smart helmet services, it is crucial to emphasize the empathy and competence of AI to boost in-role employee service performance. In contrast, in contexts with a lower level of employee–AI partnership, like in-vehicle AI services, the focus should be primarily on enhancing the competence of AI.Originality/valueThe present paper reveals the critical role of employee–AI interaction fluency in shaping both in-role and out-of-role employee service performance in the smart helmet and in-vehicle AI service contexts. Moreover, this study also validated the significant negative moderating role of self-efficacy in the relationship between interaction fluency and employee service performance.

Die folgenden Auszüge zu KI und Ethik stammen aus einem UNESCO-Dokument, das im Rahmen einer internationalen Konferenz in Peking 2019 entstand. Sie sollen auf die weltweite Bedeutung der Debatte um KI und Ethik aufmerksam machen. BEIJING CONSENSUS ON ARTIFICIAL INTELLIGENCE AND EDUCATION Preamble 1. We, the participants of the International Conference on Artificial Intelligence (AI) and Education, including 50 government ministers and vice ministers, as well as around 500 international representatives from more than 100 Member States, United Nations agencies, academic institutions, civil society and the private sector, met in Beijing, People’s Republic of China, from 16 to 18 May 2019. [...] 2. We reaffirmed the commitment made in the 2030 Agenda for Sustainable Development, particularly Sustainable Development Goal (SDG) 4 and its targets, and discussed the challenges faced by education and training systems in achieving SDG 4. [...] 6. We also recognize the distinctive features of human intelligence. Recalling the principles set forth in the Universal Declaration of Human Rights, we reaffirm UNESCO’s humanistic approach to the use of AI with a view towards protecting human rights and preparing all people with the appropriate values and skills needed for effective human-machine collaboration in life, learning and work, and for sustainable development. 7. We also affirm that the development of AI should be human-controlled and centred on people; that the deployment of AI should be in the service of people to enhance human capacities; [...] and that the impact of AI on people and society should be monitored and evaluated throughout the value chains. [...] AI to Empower Teaching and Teachers 12. Be mindful that while AI provides opportunities to support teachers in their educational and pedagogical responsibilities, human interaction and collaboration between teachers and learners must remain at the core of education. Be aware that teachers cannot be displaced by machines, and ensure that their rights and working conditions are protected. [...] Ensuring ethical, transparent and auditable use of education data and algorithms 28. Be cognizant that AI applications can impose different kinds of bias that are inherent in the data the technology is trained on and uses as input [...]. Be cognizant of the dilemmas of balancing between open access to data and data privacy protection. Be mindful of the legal issues and ethical risks related to data ownership, data privacy and data availability for public goods. [...] 29. Test and adopt emerging AI technologies and tools for ensuring teachers‘ and learners’ data privacy protection and data security. Support robust and long-term study of deeper issues of ethics in AI, ensuring AI is used for good and preventing its harmful applications. Develop comprehensive data protection laws and regulatory frameworks to guarantee the ethical, non-discriminatory, equitable, transparent and auditable use and reuse of learners’ data. 30. Adjust existing regulatory frameworks or adopt new ones to ensure responsible development and use of AI tools for education and learning. Facilitate research on issues related to AI ethics, data privacy and security, and on concerns about AI’s negative impact on human rights and gender equality. https://unesdoc.unesco.org/ark:/48223/pf0000368303 [Zugriff: 21.09.2020]

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.

This study explores the transformative role of Artificial Intelligence (AI) in climate change mitigation, focusing on its potential to enhance predictive modeling for mitigation strategies and promote environmental justice. AI's ability to process vast amounts of data quickly and accurately enables improved predictive models that forecast land-use changes, climate impacts, and environmental risks. These models play a crucial role in identifying vulnerable areas, monitoring deforestation, tracking urban sprawl, and forecasting the effects of climate change. The study emphasizes how AI can inform more effective and targeted mitigation strategies across sectors such as agriculture, energy, and urban planning. Additionally, the study highlights AI's capacity to support environmental justice by providing marginalized and vulnerable communities with real-time data, empowering them to participate in climate-related decision-making processes. AI's role in identifying communities most at risk of environmental degradation ensures that mitigation efforts address social inequities and promote more inclusive and equitable outcomes. Despite these opportunities, challenges such as data accessibility, infrastructure limitations, and ethical concerns need to be addressed for AI to reach its full potential in climate change mitigation. The study concludes that AI, if deployed responsibly and inclusively, can significantly contribute to sustainable development, enhancing both climate resilience and social equity in the fight against climate change.