AI and Data Technologies in Advancing Sustainable Development Goals

By on June 15th, 2024 in Articles, Artificial Intelligence (AI), Editorial & Opinion, Environment, Ethics, Health & Medical, Magazine Articles, Social Implications of Technology, Societal Impact

Juste Rajaonson and Ketra Schmitt


Welcome to this Special Issue on Advancing Sustainable Development Goals with AI This issue features research on how AI and data technologies can be used to advance the United Nations (UN) sustainable development goals (SDGs) [1].

Artificial intelligence (AI) and data technologies, such as machine learning, natural language processing, the Internet of Things, and other data-driven analytical techniques, are transforming various industries by providing advanced data analysis, automation, and data-driven decision-making capabilities. Encompassing a broad range of computational tools, systems, and methodologies, such advanced technologies remain imperfect due to various inherent limitations arising from biases in the training data, lack of transparency and interpretability in decision-making processes, and their environmental footprint, among other factors [2].

Despite their limitations, AI and data technologies are valuable tools for supporting efforts across various sectors, communities, and countries to meet the SDGs. These goals are complex and multifaceted, aiming to address a broad spectrum of global challenges, including poverty (SDG 1), inequality (SDG 10), climate change (SDG 13), environmental degradation (e.g., SDG 14 and SDG 15), and to ensure peace and justice (SDG 16). Overcoming such challenges, whether collectively or individually, requires analyzing and synthesizing large volumes of data from multiple sources to determine where to target policy measures and address funds and resources. Given that available data varies in type, origin, and quality, AI and data technologies are well-suited for handling such complexity by offering sophisticated methods for data processing, analysis, and interpretation.

As we will explore in this special issue, AI and data technologies’ ability to harness and interpret large and varied amounts of data shows the potential to overcome constraints that currently limit progress on achieving the SDGs, including technological capability, funding, and human resources. AI and data technologies’ interpretive capabilities can aid both public and private organizations working toward the SDGs from local to international levels. However, their deployment must be approached with caution, ensuring their benefits are maximized responsibly.

Special Issue Outline

This special issue was inspired by a session at the International Conference of Sustainable Development (ICSD), 18–23 September 2003 titled “Leveraging Open Data and AI to Measure SDG Progress in Resource-Limited Settings.” This hybrid event was based at Columbia University in New York City, USA. This conference aimed to bring together stakeholders from around the world to share scholarship and practice on achieving the SDGs. Representatives came from around the world and from a wide variety of areas including government, industry, academia, civil society, and UN agencies. In the session, participants highlighted the multifaceted roles of AI and data technologies in advancing diverse SDGs, demonstrating their valuable contributions across various sectors. From enhancing waste management practices to transforming agricultural landscapes, and from leveraging open data for precise decision-making to utilizing AI and data technologies for ethical investment in the venture capital sector, each of the five papers included in this issue showcases applications at different stages of SDG planning, implementation, and monitoring. The value of AI and data technologies in overcoming sociotechnical constraints is highlighted, while the challenges of data scarcity, biases, ethical considerations, and the need for energy-efficient computing are also recognized.

The articles in this special issue focus on the connected nature of the SDGs and the need to address them using community-based approaches and span essential steps for widespread SDG attainment, including planning, implementation, and monitoring.

The first article titled “OpenWasteAI—Open Data, IoT, and AI for Circular Economy and Waste Tracking in Resource-Constrained Communities” [A1] is written by Faisal Shennib, Ursula Eicker, and Ketra Schmitt. The authors discuss the significant data gaps in the current waste management industry at the community level and introduce the Montreal-based OpenWaste project. OpenWaste aims to leverage AI and IoT to enable more efficient waste tracking and management at the community level. The authors advocate using technologies to encourage sustainable waste behaviors, increase recycling rates, and support circular economy initiatives. This is especially valuable in cases where data is lacking, and manual tracking and follow-up would be time-consuming and resource-intensive. Additionally, there is a need for scalable solutions to make these efforts effective on a larger scale. The article presents a case study planned for the Concordia University campus in Montreal, QC, Canada, while discussing potential project improvements.

The second article is written by Alex Roig Albelda, who coordinates the 2030 agenda for the Valencian Federation of Municipalities and Provinces (Federación Valenciana de Municipios y Provincias). “Enhancing Sustainability in Resource-Limited Environments: Government, Culture, and AI” [A2] highlights a practical application of AI in Spain’s Valencian Community. This initiative seeks to link environmental and socioeconomic data and information to tackle critical challenges such as drought, soil degradation, and rural depopulation. This article illustrates how AI technologies can facilitate advancements in food production (SDG 2), water management (SDG 6), and biosphere conservation (SDG 15) by employing predictive analytics for environmental forecasting and precision farming to enhance agricultural productivity. Given the financial constraints and lack of data and information faced by the agricultural sector and various local government agencies working with rural communities, AI technologies emerge as a valuable tool for policy integration, facilitating cost-sharing and knowledge exchange among stakeholders.

Transitioning to the monitoring phase, Apurva Kulkarni and colleagues at the International Institute of Information Technology Bangalore (IIIT Bangalore) authored “Toward Sustainable Data Practices: Integrating Open Data With SDG-Based Data Lake Frameworks” [A3]. They tackle the challenge of leveraging open data for SDGs through a novel datalake framework. The article is relevant to this Special Issue for demonstrating how targeted data retrieval can significantly enhance decision-making processes in the context of SDGs, especially in the agricultural sector, a crucial sector in developing countries. By providing a system that aligns open data with SDG criteria and incorporates domain-specific and location-aware data, the framework ensures that policymakers and domain specialists can access relevant and actionable information based on AI models. Such an approach optimizes resource use and improves the efficacy of policy formulation and AI-driven analyses related to SDGs, showcasing the critical role of precise data management in advancing global sustainability efforts.

Continuing with the theme of monitoring, Hossein Masoumi Karakani from the University of Pretoria presents a novel approach leveraging AI and open data to enhance SDG monitoring, particularly in a developing country context. The article “Supporting the Measurement of Sustainable Development Goals in Africa: Geospatial Sentiment Data Analysis” [A4] uses Geospatial Sentiment Analysis, to process tweets. The study generates real-time dashboards reflecting public perceptions of SDGs, related organizations, and policies. Such an approach enables data-driven decisions and policy interventions, offering an innovative way to complement traditional data collection methods with real-time, location-specific public sentiment analysis to overcome sociotechnical constraints. While acknowledging the potential of Geospatial Sentiment Analysis, the article also discusses the limitations and ethical considerations, emphasizing the need for careful implementation and integration with traditional methods to ensure comprehensive and representative data for SDG monitoring.

In the final article of the series, Beatriz Sasse, a research consultant for Ecorys, demonstrates the importance of evaluating the interconnected effects of various policies on the SDGs, while highlighting the relevance of AI-based network analysis in achieving this goal. “The Interdependence of the Sustainable Development Goals: Network Analysis as a Methodology for Policy Impact Evaluation” [A5] concludes the series by discussing the broader usage of AI in sustainability policies and examining the current state of AI applications in environmental monitoring, policy enforcement, and sustainable resource management. The author highlights both opportunities and challenges and discusses important ethical considerations, data privacy concerns, and the need for transparent methodologies in AI implementations. The conclusion emphasizes the need for interdisciplinary collaboration, robust regulatory frameworks, and stakeholder engagement in leveraging AI for integrated sustainability policy.

The articles in this special issue focus on the connected nature of the SDGs and the need to address them using community-based approaches and span essential steps for widespread SDG attainment, including planning, implementation, and monitoring. While the steps formally explored in this special issue are essential, they are not sufficient for long-term success in adhering to the SDGs. Implicitly, the monitoring steps and open-data exploration in this special issue conform to the plan–do–check–act (PDCA) cycle, commonly used to implement continuous improvement. This principle is a key component for the sustainable advancement of technology. Formally embracing continuous improvement can help to ensure that methodologies and applications of AI and data technologies remain responsive and adaptable to new challenges and insights. This principle complements the collaborative and ethical framework outlined by the authors. It ensures that strategies for achieving the SDGs in various organizational settings are perpetually evolving, enhancing our capacity to drive meaningful change.

Current Wave of AI and Data Technologies in Advancing SDGs

As we reflect on the examples provided in this special issue, showcasing AI and data technologies’ role in addressing the SDGs, it prompts us to examine the broader context—the rapid pace and extensive scope at which these technologies are being applied to SDGs. AI and data technologies are advancing in both sophistication and the range of problems they address, including potential applications in sustainability. Examples of these opportunities include enhancements in data collection and analysis within environmental sectors, the development of sustainable business models through data analytics, reducing environmental footprints via precise tracking systems, and offering advanced, place-based policy analysis to achieve the SDGs collectively through strategic AI applications [3].

In this context, the AI for Social Good movement provides numerous examples where AI and data technologies are harnessed to advance a wide range of SDGs through targeted, ethical applications [4]. Initiatives and projects under this movement include employing machine learning for early disease detection and personalized treatment plans to advance good health and well-being (SDG 3), leveraging data analytics for climate action and environmental monitoring (SDGs 13, 14, and 15), and utilizing AI-driven platforms for adaptive learning to enhance education quality (SDG 4). In addressing social welfare, predictive analytics are used to identify individuals at risk, aiming to reduce poverty and inequality (SDGs 1 and 10), while in agriculture, AI technologies optimize farming practices to ensure food security (SDG 2). In urban development, AI and advanced technologies are also being used to improve service delivery and build climate resilience. This involves proper tracking systems and advanced data monitoring, which contribute to SDG 11 on sustainable and resilient communities [5]. Similarly, the application of AI within the water–energy–food nexus is also recognized for its capacity to foster sustainable and responsible business models through an integrated approach, promoting SDGs through institutional and innovation theories [6].

Imperfect, Yet Valuable Tools Under Sociotechnical Constraints

While studies on public perception generally show a positive attitude toward AI and data technologies’ potential to contribute to the SDGs [7], the effectiveness of these technologies depends on the availability of large, high-quality data sets. Data scarcity and potential training biases can hinder AI’s accuracy, leading to outcomes that do not align with SDG targets and can even exacerbate existing inequalities. Additionally, AI technologies require substantial computational resources, which can be energy-intensive and contribute to environmental concerns. This highlights the need for more energy-efficient computing paradigms to mitigate the carbon footprint of training and running sophisticated AI models. Finally, ethical considerations such as privacy and security necessitate careful management. Ensuring that AI technologies are developed and deployed in a manner that respects individual rights is critical for their acceptance and effectiveness in advancing SDGs [8].

AI and data technologies are advancing in both sophistication and the range of problems they address, including potential applications in sustainability.

However, sectors, communities, and countries faced with key sociotechnical constraints such as limited financial and human resources and weak technological capabilities, make AI technologies’ application to SDGs relevant. As we will see in this special issue, AI and data technologies address these challenges by enhancing efficiency, reducing operational costs, augmenting human capabilities, bridging access and equity gaps, and facilitating sustainable solutions.


This special issue features a range of studies demonstrating how AI and data technologies can be applied in various sectors, including waste management and agriculture. The articles included here also highlight the phases of sustainability in which AI can be deployed, including implementation, predictive analytics to better inform sustainability action, monitoring, and program evaluation. The articles included in this special issue lay out the case for implementing AI in sustainable development applications. As these articles demonstrate, AI and data technologies can help organizations contribute to the SDGs, but their potential benefit continues to be limited because of data biases, ethical concerns, and the environmental impact of their deployment. The claims made in this collection echo many authors who suggest that AI and data analytics, more generally, can improve decision-making, processes, and resource distribution. The results shared here are encouraging in this regard—better data, models, and analytics can deepen the understanding of processes and help decision-makers identify better courses of action.

AI approaches have important limitations. Because any machine-learning approach has to be trained on data, the systemic biases embedded in the training data will be replicated in machine-learning predictions unless effective steps are taken to overcome the biases. AI trained on biased data will replicate the correlation found in that data and thus make predictions that can perpetuate discriminatory results [9]. This bias can have serious consequences for algorithmic decision-making. For example, sentencing decisions based on machine learning have been shown to increase racially discriminatory outcomes [10], [11]. These discriminatory outcomes directly worsen societal issues addressed by the SDGs. While the limitations imposed by training data are specific to machine-learning techniques, a more serious limitation impacts AI improvements or any proposed policy or implementation improvement: AI alone cannot force human decision-makers or institutions to make better decisions or enact better policies. No matter how accurate or powerful the model is, some person or institution must choose to enact the recommendations.

The special issue highlights cases of discrete decisions, where automation of classification processes is a key improvement that could lead to efficiency gains, as seen in the computer vision-assisted sorting outlined in [A1], or the classification processes used in sentiment analysis outlined in [A4]. It is also clear that data-processing improvements, as outlined in [A5] as well as [A3], have the potential to identify decision spaces that could lead to better outcomes. But critically, these recommendations need to be adopted. Perhaps, the best way forward with AI and data technologies is shown through Roig’s piece [A2] that explored the case of Valencian agriculture, where a mix of regional organizations collect and deploy data to guide collective decision-making around crops, irrigation, and sustainable policies. Here, AI and data collection are directed by the people who use and benefit from the decisions. This community-led approach means that AI is just another tool for the collective management of resources.

This collection demonstrates how AI and data technologies can allow decision-makers to find opportunities and actionable insights in their data and practice, especially amid various sociotechnical constraints illustrated in this issue.

Ethical considerations, transparent methodologies, and precise data management while deploying AI and data technologies are fundamental ingredients of successful AI implementation. To ensure the effectiveness and benefits of these technologies in the context of SDGs, it is paramount to maintain data integrity, respect privacy, and address biases.

The articles in this issue underscore a crucial theme: the importance of engaging diverse stakeholders, recognizing the interconnectedness of SDGs, and promoting collaboration for navigating sustainable development complexities. For AI and data experts, this entails a commitment to ethical practices, interdisciplinary collaboration, and an understanding of the broader sociotechnical context to maximize the impact of their work.

Numerous studies are being developed regarding the technological dimension of continuous improvement in the field of AI and data technologies in terms of software process improvement [12], self-improving AI systems [13], and human–AI collaboration [14]. Yet, their applications in achieving the SDGs open new perspectives. Here, continuous improvement is not solely about efficiency and effectiveness; it also involves addressing multiple constraints and tradeoffs. This requires incremental technological innovations as well as significant changes at the organizational and behavioral levels. For instance, implementing AI-powered public transport systems to reduce congestion and greenhouse gas emissions in urban transportation depends on city planners redesigning urban spaces to prioritize public transport and pedestrians. Furthermore, residents must adapt their commuting habits, such as opting for public transport over personal vehicles, supported by policies and infrastructure changes that encourage these behaviors. Similarly, integrating AI into sustainable agriculture requires technological solutions for precision farming that minimize resource use and necessitates farmers adopting new practices, competencies, and attitudes toward these innovations.

As we look to the future, it is clear that the prospect of achieving the SDGs through AI and data technologies is both promising and challenging. The examples provided in this special issue highlight the complex nature of this pursuit, emphasizing the necessity of an approach that is as dynamic and adaptive as the technologies we seek to harness. Promoting a culture of continuous improvement and open collaboration across various disciplines ensures that efforts to leverage AI for sustainable development are not only effective, but also inclusive, ethical, and responsive to the changing needs of society and the environment.


We would like to thank all the contributors, peer reviewers, and supporting institutions/organizations for their essential contributions toward completing this special issue. Their expertise, thorough reviews, and unwavering support have been extremely valuable in shaping the comprehensive discussions on AI, data technologies, and SDGs presented here.

Appendix: Related Articles

[A1] F. Shennib, U. Eicker, and K. Schmitt, “Openwaste NA proposal for an open data, IoT and AI-driven framework to drive community-level circular economy and reach zero waste targets,” IEEE Technol. Soc. Mag., vol. 43, no. 1, pp. 39–53, Mar. 2024, doi: 10.1109/MTS.2024.3372610.

[A2] A. Roig Albelda, “Enhancing sustainability in resourcelimited environments: Government, culture and AI,” IEEE Technol. Soc. Mag., vol. 43, no. 1, pp. 54–61, Mar. 2024, doi: 10.1109/MTS.2024.3365574.

[A3] A. Kulkarni, C. Ramanathan, and V. E. Venugopal, “Toward sustainable data practices: Integrating open data with SDG-based data lake frameworks,” IEEE Technol. Soc. Mag., vol. 43, no. 1, pp. 62–69, Mar. 2024, doi: 10.1109/MTS.2024.3365591.

[A4] H. M. Karakani, “Supporting the measurement of the sustainable development goals through the use of geospatial sentiment analysis: A case study of Africa,” IEEE Technol. Soc. Mag., vol. 43, no. 1, pp. 70–85, Mar. 2024.

[A5] B. Sasse, “The interdependence of the SDGs: Network analysis as a methodology for policy impact evaluation,” IEEE Technol. Soc. Mag., vol. 43, no. 1, pp. 86–90, Mar. 2024, doi: 10.1109/ MTS.2024.3365592.


Author Information

Juste Rajaonson is a geographer and professor with the Department of Urban Studies and Tourism, University of Quebec, Montreal, QC H2X 3X2, Canada. Email:

Ketra Schmitt is an associate professor at the Centre for Engineering and Society, Concordia University, Montreal, QC H3G 1M8, Canada. She is also an associate member at Concordia
University. She is currently the Editor-in-Chief of IEEE Technology and Society Magazine and serves as a board member for the IEEE Society for the Social Implications of Technology.


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