Research Objectives
To analyse AI’s role in fostering sustainability and resilience in the modern world. Explore ethical concerns surrounding AI, such as algorithmic bias, data privacy, and potential job displacement.
Keywords
AI, Sustainability, Resilience, Ethics, Transformation
Bio
Roxanne Boodhoo is an accomplished professional with a diverse and versatile background. Her extensive academic training has equipped her with various skills and knowledge, enabling her to excel in multiple roles. Roxanne is known for her strong work ethic, diligence, and commitment to undertaking any responsibilities assigned to her. She is deeply passionate about helping and supporting others, making her a compassionate and empathetic individual. Throughout her career, Roxanne has consistently demonstrated a dedication to making a positive impact through her professional work or community involvement, striving to uplift those around her.
Abstract
As artificial intelligence (AI) permeates the digital world, its transformative potential for a sustainable and resilient future is undeniable. This review examines the existing literature on AI applications, exploring both their transformative capabilities and the ethical challenges that must be addressed.
Aim: To analyse AI’s role in fostering sustainability and resilience in the modern world. The review investigates how AI can be leveraged to address environmental challenges, optimise resource management, and reinforce societal preparedness for disruptions. It concurrently explores ethical concerns surrounding AI, such as algorithmic bias, data privacy, and potential job displacement. The study employed a systematic review of academic research on the intersection of AI, sustainability, and resilience. The analysis focuses on AI applications in areas like climate change mitigation, disaster response optimization, and its contribution to building a more sustainable future.
This study highlights AI’s potential to revolutionise sustainability efforts. This includes advancements in renewable energy production, disaster prediction modelling, and personalised environmental solutions. While acknowledging AI’s transformative power, the study emphasises the need for robust ethical frameworks. Mitigating bias in AI development, ensuring data privacy, and fostering human-centric AI applications are critical for maximising positive outcomes.
AI presents a powerful tool for building a more sustainable and resilient future. However, responsible and ethical development is paramount. The review underscores the importance of continuous research and collaboration to harness AI’s potential while mitigating its risks, ultimately creating a future where technology and ethics work in harmony.
Introduction
The fundamental objective of this paper is to address the potential of AI in a global landscape and the challenges of AI ethics to determine the path for a sustainable and resilient future. We are going through rapid technological advancements in AI which is driven by big data, powerful computing infrastructure, and advanced software algorithms. The rapid development of AI can indeed greatly facilitate tackling global challenges such as healthcare, lifestyle, agriculture, energy, safety, and infrastructure. But this fast-growing technology merely makes it an extremely challenging task to stay current with and effectively guide the changes with respect to societal values and ethical behaviour. The worldwide acceleration of AI research and applications has ignited a need for institutional, regulatory, and legal initiatives to ensure that AI development and deployment are not only compliant with laws and regulations but also sensible to robust ethical considerations (Kazim & Soares Koshiyama, 2027) (Dignum, 2022) (Pastor- Escuredo, 2020).
Artificial intelligence (AI) research aims at creating systems capable of making human-like inferences, achieving goals with minimal human intervention, and exhibiting flexible, autonomous behaviour in real-world environments. AI is revolutionising our day-to- day activities concretely through the extensive data collection that enables machine learning, deep learning, natural language processing, and visual recognition from internet search engines, game- playing automata, chatbots, driverless cars, and robot caregivers. Human interaction with AI systems occurs through social platforms such as the Google search engine, Facebook, Twitter, Instagram, LinkedIn, and so forth, where AI algorithms filter content, create posts, and manage interactions. As a society, we are progressively embedding AI in our lives with expectations of improved quality of life.
2. Aim Various anticipate majority of artificial intelligence used today are relaxing experience in areas such as loan qualification, job interviews for decision making, media and customer controlling, leading to benefits such as risk, reduction, improved utilisation of resources, enlarged personalisationand a more convenient and seamless overall experience. However, it is undebated that there also are numerous significant concerns ago this quick brake association among unmarked AI. These embody a deficit on transparency, fairness, quality and accountability, excessive data protection and the registers of ambivalent suggestions which might innocently give unequitable personal analysis. It’s no surprise the testing of misuse such artificial intelligence has opened an encompassing creative intelligence to power regulators in different parts of the globe. If AI transformations manage well meet this enthusiasm and among this publication we provide views and consent specifications to that sign, it should become practical in achieving numerous global sustainable advancement signals something which broadly do many think is possible, in theory.
Artificial Intelligence or AI adoption in numerous digital capacities is growing at a semiautomatic pace. The adoption is mainly due to its potential to transform and streamline numerous digital processes including information recognition, recommendation and deduction. These characteristics have led to increasing use of AI technologies in diverse areas, including autonomous vehicles, advanced communication tools, and robotics. In addition to this, some of the recent developments in AI along with the imminent start of the 5G-oriented mobile era are projected to speed up the overall growth of AI around the globe. In fact, the already growing popularity of AI technologies in emerging applications and involving business emergent has further fueled the growth of artificial intelligence. According to a recent market study, the overall market development for AI is predicted to grow at a CAGR of whether 40.7% between 2020 and 2026. This rapid growth is directly associated with specifically drive uses of AI, majority by many firms, purporting to realise a high return on their investment. It is due to the increasing growth of AI technologies, that we need to comprehend their marketing journey.
Data Collection
The data collection scheme for this study consists of two parts: (1) the secondary collected electronic works, industries reports, and analysis and (2) selected the comparative case study actual practices (Li et al., 2022). A wide range of quantitative and qualitative data collection from secondary sources are considered. Its major limitations notwithstanding because the data were publicly available. Method for the primary data collection is employed through a structured interview with project in-charge or/and process and policy owner of not more than ten of the selected case artist profiles with a maximum of two respondents where required. The number of interviews was conceived to achieve maximum diversity in terms of the artists, gender and their talents. The interviews were semistructured with open-ended questions and were conducted en face in a quiet location, to ensure an efficient and comfortable atmosphere of dialogue. The collected data are subsequently analysed in measurement ranges and rate agreement of hypothesis test and verified by the expert comments by analyse into the consequent statement.
This section presents an overview of the research method that was employed in this study. The method followed in this research attempts to combine diverse approaches in gathering evidence and insights to put forward mixed design and to increase the interpretation that is derived. Proposed research will be following mixed method; the concurrent approach of Mixing applies to different kinds of research in different ways (Muhlenbach, 2020).
Results and Discussion
AI seems to be focused neither on data acquisition and quality aspects, nor on the secure management of data.
It is seen as important, although their importance rate from the respondents (50.00%) can be evaluated as only moderate. Here AI acts as a “blind” method. This means all methods in that area just use the primary heuristic information from input signal, as it does not reduce asymmetry of information during the decision-making process. For “enlightenment” a deeper AI-extended method and mode of cooperation between collaborative humans and such system configuration, which has a low complexity in content and information management as in (Bano et al., 2023). We point out that the system can make decisions at different levels without the explanation of “black box” and the AI- supported modes of cooperation in group systems.
Although the overarching goal is to manage the digital transformation, the AI projects help digital transformation projects to a higher degree (Giralt Hernández, 2024). Approximately 79% of all interviewees estimate that AI projects support the primary digital transformation initiatives, and over 35% of the participants even believe that they have a very high alignment. The digital transformation management challenges of an AI- enhanced project management that result from these findings include the following: AI-enhanced project management should reflect external and internal signs; there are calls for an AI-enhanced project management design that supports organisational agility; it is important to integrate AI project management and the primary digital transformation initiatives; the evaluation of guidelines, models, and standards for AI security and ethics has to be embedded in everyday project work and is rarely a core task of project management planning; and an increased effort is expected in PMI areas in AI- enhanced project management. AI as an evolutionary approach requires the closer examination of the AI- related epistemic ethical decision process at a number of points and key challenges (Samuel et al., 2021).
4.1 AI Applications in Climate Change Mitigation
AI technology can potentially spur innovations and policy solutions by identifying relationship patterns between causal and impactful sequences societal processes and environmental outcomes. It can uncover intricate and non-linear dependencies between climate-relevant circuits, like greenhouse gas emissions, atmospheric parameters, and climate conditions, including seasonal variations and the long-term climate change, evolution. In relation to AI in the digital era, AI models and big data analysis tools have been used to successfully warn people and organise rescue teams before and after natural disasters before extensive loss occurred. AI has also been used in urban planning and to analyse and assess future climate-related challenges, like housing, social, and eco-healthy construction and living conditions. AI technology in health care, namely in PM- related fields potentially acts as the most critical segment of the SDGS’ achievement. Technology can be harnessed even further for recognising and addressing a globally critical societal change factor, subject to climate change.AI can enhance monitoring of wildfire-prone regions and help technological development of fire suppression and rehabilitation strategies (Sirmacek et al., 2022) (Cowls et al., 2021). AI has been applied to early detection and real-time disaster relief support through layer processing of SAR and thermal infrared (TIR) satellite imagery. Research has shown that AI can improve the prediction accuracy of floods, enhance water quality monitoring, and optimise water supply networks and complex chemical models. AI models have been deployed to help corporations reduce greenhouse gas emissions, to manage organisational carbon footprint data, and to forecast methane and CO2 emissions from fossil fuel combustion and land use change.
4.2 AI in Disaster Response Optimisation
Network-oriented AI of the future of electric systems can increase user-centricity and situational awareness, considering all users equally in the future energy regime, to minimise differences between producer, control centre, government, and user. To derive these goals, the paper delves into social identity theory, contextualising the convergence between social relations and technology in the network-oriented and transactive electric systems. The corona pandemic in 2020 underlined the challenges and vulnerabilities in global emergency management and yet emphasises the necessity of connectedness, the awareness of the other, and the global character of humanity. Success in future systems, such as network- oriented governance, will not only depend on the behind- the-scenes intelligence and general rules, but also the prevalent integration of the users and operators of network-owned assets (Pastor-Escuredo, 2021).
The recent development in renewable and decentralised energy resources are a result of emerging global problems, such as resource scarcity and infrastructure weakness, along with advances in digital and artificial intelligence technology. These technologies enabled the transformation of the current centralised energy systems into decentralised, network-oriented, intelligent micro-grids, in addition to challenging big energy corporations as well as small- scale energy cooperatives. Using a social identity perspective, this research aims to investigate the contribution of network- oriented AI that governs beyond corporate management, incorporating the understanding, integration of users, smoothing out differences, and achieving security and sustainability between all the players in and around the grid (Lee et al., 2022).
4.3 AI’s Contribution to Building a More Sustainable Future
Regarding these considerations, it is hypothesised that AI technologies can become integral to achieving the UN’s Sustainable Development Goals (SDGs) by strengthening responses to AI-related challenges, in particular, by orchestrating mechanisms which allow computational decision-aids for decision-critical areas of human societies, also balancing transformations and ethical considerations as a dwelling environment for future generations. Furthermore AI can assist in developing a more sustainable portfolio of everyday life choices and sustainable options ,. Moreover, it fosters changes to the very cultural landscape in order to integrate these options as common/ingrained, broadening its perspective. Now, to work towards a technically sound sustainable AI, a comprehensive toolkit of sustainability criteria and indicators for AI system design-as well as management decisions has to be elaborated from a holistic and theoretically and empirically validated perspective.
AI and broader data science methods and technologies have attracted interest across multiple sustainable development sectors (Rohde et al., 2023) (Hermann et al., 2021). Among potential building blocks for sustainability, research efforts have focused on designing and developing AI algorithms, digital solutions, technologies and applications, such as healthcare data classification. In another realm, environmental monitoring systems integrating, for example, Internet of Things (IoT), remote sensing, and data
analytics, aim to obtain and interpret environmental information. Figure 1 illustrates the four foundational technologies underpinning IoT applications in the Metaverse are:
Responsible AI: This enables engineers and data scientists to clearly understand and explain the inner workings of AI algorithms.High-Speed Data Communication: This supports real-time connectivity through advanced technologies like millimeter wave (mmWave), non-orthogonal multiple access (NOMA), massive multiple-input and multiple- output (MIMO), terahertz (THz), and visible light communication (VLC).
MEC (Multi-access Edge Computing): This provides the Metaverse with substantial server resources to sustain the virtual 3D environment and offers a network with ultra-low latency for nearly instantaneous responses.
Digital Twins: These create virtual replicas of physical objects or services, delivering users an immersive experience that mirrors the real world.
Hence, AI offers opportunities to develop and maintain sustainable and balance ecosystems. Moreover, AI might solve deep societal issues and ensure common welfare as a response to anticipated challenges in an ever-changing and interconnected world. For business stakeholders, it supports new product development, competitive disruption and risk management.
Conclusion
AI, if properly harnessed and combined with human compassion and communication, will drive a monumental leap forward for cancer care in the UK and beyond. It is however possible that the speed of technological development has overtaken the pace of professional moral and ethical education, as society has not evolved at such a tempo (LaCroix & J. D. Prince, 2023). Consequently, the balance of technological development with the cultural, moral, and ethical complexity of our world is at risk of falling into disarray. The Allen Fairbairn Vision for Ethics Series Paper took the opportunity to consider the ethical implications across a range of healthcare settings for AI using the Stackelberg Anticipative Game formalism with a mean field approach. Key perspectives from international contributors are used to explore ethical considerations in oncology. Suitable definitions, and argument, are advanced for the necessity of a robust prioritisation framework that adapts to evolving data. Ethical frameworks necessitating observing the basic tenets of responsible AI, addressing biases, highlighting the ethical need for the translation of a “trust understanding” and Ability conditions are considered.
The integration of AI in various domains, including healthcare, brings unprecedented potential for human advancement, pioneering new treatment modalities, supporting prioritisation and management of patient care, as well as enabling operational efficiencies and cost savings (Fabrice Djete, 2023). The value has been considerably recognised as solutions have been used to save lives, particularly during the COVID-19 response, and are potentially pivotal for delivering care during future pandemics. It opens up exciting possibilities for translation of AI system / technology integration and will require further queries into safe and effective deployment of advanced digital applications in “routine” practice from an efficiency, economic and ethical standpoint. Amongst the key ethical considerations are organisational responsibility for maintaining standards to safeguard informed consent, ensuring human oversight of AI decisions, transparency in AI decision-making, and addressing biases and impact on workforce (Zhu et al., 2021).
References
Bano, M., Zowghi, D., Shea, P., & Ibarra, G. (2023). Investigating Responsible AI for Scientific Research: An Empirical Study. [PDF]
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: leveraging artificial intelligence to combat climate change— opportunities, challenges, and recommendations. ncbi.nlm.nih.gov
Dignum, V. (2022). Relational Artificial Intelligence. [ PDF]
Fabrice Djete, M. (2023). Stackelberg Mean Field Games: convergence and existence results to the problem of Principal with multiple Agents in competition. [ PDF]
Giralt Hernández, E. (2024). Towards an Ethical and Inclusive Implementation of Artificial Intelligence in Organizations: A Multidimensional Framework. [PDF]
Hermann, E., Hermann, G., & Tremblay, J. C. (2021). Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability. ncbi.nlm.nih.gov
Kazim, E. & Soares Koshiyama, A. (2021). A high- level overview of AI ethics. ncbi.nlm.nih.gov
LaCroix, T. & J. D. Prince, S. (2023). Deep Learning and Ethics. [ PDF]
Muhlenbach, F. (2020). A Methodology for Ethics-by- Design AI Systems: Dealing with Human Value Conflicts.
Pastor-Escuredo, D. (2020). Ethics in the digital era (PDF)
Pastor-Escuredo, D. (2021). Future of work: ethics. [PDF] Rohde, F., Wagner, J., Meyer, A., Reinhard, P., Voss, M., Petschow, U., & Mollen, A. (2023). Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems. [PDF]
Samuel, G., Chubb, J., & Derrick, G. (2021). Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research. ncbi.nlm.nih.gov Sirmacek, B., Gupta, S., Mallor, F., Azizpour, H., Ban, Y., Eivazi, H., Fang, H., Golzar, F., Leite, I., I. Melsion, G., Smith, K., Fuso Nerini, F., & Vinuesa, R. (2022). The potential of artificial intelligence for achieving healthy and sustainable societies. [PDF]
Zhu, L., Xu, X., Lu, Q., Governatori, G., & Whittle, J. (2021). AI and Ethics — Operationalising Responsible AI. [ PDF]
