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Opportunities and Risks in AI Development

Harnessing AI innovation for social good demands cooperative governance to align technology with ethics and shared values, manage impacts on employment, balance environmental considerations, and democratize decisions through multi-stakeholder collaboration - guiding progress towards sustainable development for all.
AI trends and risks

Artificial intelligence (AI) has captivated humankind’s imaginations for generations, with visions of building thinking machines that match or even transcend human capabilities. The concept can be dated back to 1950 when British mathematician Alan Turing first posed the question: “Can machines think?” The past decade has seen monumental leaps toward an answer, with AI programs mastering complex games, generating creative content, and automating an ever-wider range of analytical and physical tasks across industries. The present-day AI boom is fueled by advancements in machine learning, explosive growth in computing power, and the availability of big data, along with intense competition between tech powerhouses racing to profit from an emerging general-purpose technology. AI investment has hit record levels in 2023 and shows no signs of slowing down as businesses and governments bet it will drive the next phase of digital and economic transformation.1 But as commercial deployment extends further into domains such as healthcare, transportation, and financial and public services, questions arise on how to ensure the technology aligns with the greater good while avoiding potential pitfalls.

AI Governance in Today's World

The landscape of AI governance and regulation is evolving globally; Over 60 countries have engaged in national AI initiatives. Yet few have definitively regulated AI outside rules directly tied to data use. The EU has been proactive in introducing concrete regulatory frameworks, the April 2021 Coordinated Plan on AI being an example, as it aimed at fostering a safe and innovation-friendly environment for AI development.2 In the United States, the recent executive order by President Biden reflects a shifting perspective, tasking agencies to reconsider their approach to AI and addressing concerns relating to national security, competition, and consumer privacy while promoting innovation and public service use.3 The order's impact is not void of scrutiny; While it mandates companies developing powerful AI models to disclose safety test results, it doesn't outline the implications of reporting potentially dangerous models which has sparked debates—some experts see the order as enhancing transparency, while others question its effectiveness without clear consequences for unsafe AI. The order largely delegates tasks to other agencies, with the Office of Management and Budget tasked to guide federal agencies in managing AI risks while fostering innovation. Biden's executive order also imposes requirements on companies developing powerful AI models with potential national security threats. Defining sufficiently dangerous AI models remains a challenge, though, especially considering the escalating computational power used in training. The order sets a threshold significantly higher than current models but aims to regulate next-generation, state-of-the-art AI models. Still, uncertainties linger about enforcement mechanisms.

Conservation Efforts with AI Technology

As AI governance continues to take shape, its role in conservation and environmental protection is evident. It has the potential to help safeguard endangered species and impact ecological studies in ways we have never seen before. AI leverages data to comprehend animal behaviors, monitor animal habitats, and predict threats to their existence. Researchers at Washington State University utilized 650 camera traps across vast terrains to measure Canada lynx populations, offering critical insights for conservation efforts.4 AI-enabled systems resembling those developed by Conservation AI autonomously analyze millions of images and footage from drones or camera traps, identifying and tracking diverse species, including endangered pangolins in Uganda and orangutans in Malaysia.5 This efficient processing of tens of thousands of images hourly not only aids in wildlife monitoring but also swiftly detects threats like poaching, further showing AI's potential to safeguard vulnerable species.

AI has proven effective in analyzing soundscapes in biodiverse regions similar to the Choco in Ecuador, where it identified numerous bird, amphibian, and mammalian species from audio recordings and was able to measure the success of forest recovery efforts.6 AI models are also being used to reconstruct historical environmental changes to help link biodiversity loss in ecosystems to chemical pollution and extreme temperature events.7

Sustainability Challenges in AI Development

Amid these promising advancements, there is a concern about the environmental cost of AI development. The energy-intensive nature of data collection, processing, and AI training raises a challenging dilemma. How we balance AI’s innovation with its environmental impact is a necessary conversation to have. With climate change accelerating at an alarming rate, the need for innovative solutions is even more crucial. Yet AI systems like large language models have a massive, continuously growing carbon footprint. As companies compete to develop powerful AI applications across every industry, energy and computing demands only continue to climb. To help ease these worries, there is a growing commitment from tech giants. Google announced $10 million for projects using AI to aid victims of natural disasters and climate change, complementing their long-standing support exceeding $200 million for ethical AI development and NGO backing.8 Similarly, OpenAI has collaborated with to work on an accelerator, which, using large language models, will attempt to revamp how we approach future global development efforts.9 Despite these efforts, the question remains: are the benefits of AI in solving sustainability issues offset by the harm caused by the data and AI value chain? The intricate web of data collection, processing, and AI model training contributes significantly to carbon emissions and resource consumption.10 This raises a concern that despite noble intentions and financial commitments, the environmental toll of the data and AI cycle might challenge the efficacy of these initiatives.

Reshaping Economic Landscapes

For years, the impact of AI and automation on jobs and incomes has caused distress about displacement and job security. However, recent insights from the Institute for the Future of Work (IFOW) paint a more nuanced picture.11 Anna Thomas, the director at IFOW, showed that AI adoption within UK firms not only created new job roles but also enhanced the quality of existing positions. Surveys across more than 1,000 UK firms revealed a reassuring statistic: over three-quarters reported the creation of fresh roles due to technology adoption. This finding challenges the pervasive fear of mass job loss and depicts one of the promising aspects of AI and automation—its capacity to cultivate new employment opportunities and improve job standards for those involved.

Amidst this optimism, challenges do continue to persist. While technology adoption leads to job creation, it also poses a potential threat to existing roles, casting uncertainties about the future of work for many individuals. The World Economic Forum’s Future of Jobs Report earlier this year estimated that nearly fourteen million jobs would be eliminated by 2027, most of them due to the automation of tasks.12

While AI is frequently blamed for rising inequality and stagnant wage growth, evidence suggests that its role in this context, at least up to this point, is relatively small. Economic inequality across many countries began to surge in the 1980s, preceding significant commercial use of AI. Factors such as globalization, macroeconomic austerity, and deregulation all have played more substantial roles in shaping economic inequality than AI alone. There is also a worry about market power imbalances in the field of AI. Currently, concerns surround data monopolies, where firms with substantial data access may amass significant market control and potentially accumulate monopolistic profit at the expense of consumers, workers, and other companies, leading to an exacerbation of inequalities in the market.

AI and automation encapsulate both promise and peril; They offer avenues for job expansion and the improvement of work quality, yet the looming risks of job displacement and market power imbalances remain critical concerns. Navigating these transitions responsibly becomes imperative to ensure that the advantages of technological progress benefit everyone.

AI in the Future

Recognizing the promising potential of artificial intelligence while mitigating the risks of technology with such a vast array of possibilities requires responsible policies and extensive collaboration. As the recent executive order issued by President Biden illustrates, the United States is moving in the right direction by investing in initiatives that apply AI to advance sustainable development and global partnerships to establish effective governance norms. As we continue to progress through the AI landscape, collaborative efforts between governments and private sectors around the world must continue to grow in order to navigate the ethical and developmental dimensions of AI.


[1] Williams, L. (2023, January 6). Top technology investment trends to watch in 2023. Investment Monitor.

[2] European Commission. (2023, January 26). A European approach to Artificial intelligence | Shaping Europe’s digital future.

[3] Biden, J. “Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.” The White House, 30 Oct. 2023, www.whitehouse.geov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/.

[4] “AI for Wildlife Conservation—from an AI.” Washington State Magazine,

[5] Thompson, T. (2023). How AI can help to save endangered species. Nature.

[6] Müller, J., Mitesser, O., Schaefer, H. M., Seibold, S., Busse, A., Kriegel, P., Rabl, D., Gelis, R., Arteaga, A., Freile, J., Leite, G. A., de Melo, T. N., LeBien, J., Campos-Cerqueira, M., Blüthgen, N., Tremlett, C. J., Böttger, D., Feldhaar, H., Grella, N., & Falconí-López, A. (2023). Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests. Nature Communications, 14(1), 6191.

[7] Thompson, T. “How AI Can Help Save Endangered Species.” Scientific American,

[8] Artificial Intelligence for Accelerating Progress on the Sustainable Development Goals: Addressing Society’s Greatest Challenges. (n.d.). United States Department of State.

[9] “GenAI Accelerator 2024.”, Accessed 23 Nov. 2023.

[10] Dhar, P. “The Carbon Impact of Artificial Intelligence.” Nature Machine Intelligence, vol. 2, no. 8, 1 Aug. 2020, pp. 423–425,,

[11] Hayton, J., Rohenkohl, B., Pissarides, C., Liu, H. What drives UK firms to adopt AI and robotics, and what are the consequences for jobs? What drives UK firms to adopt AI and robotics, and what are the consequences for jobs? 2 The Pissarides Review Acknowledgements. (2023).

[12] World Economic Forum. (2023). Future of Jobs Report 2023.


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