Sustainable AI in Practice: Ethical Implications of Generative AI Systems in Arcadis
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2024-11-29
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en
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Abstract
The recent boom in generative artificial intelligence (GenAI), exemplified by tools like
OpenAI’s ChatGPT, has prompted organizations to integrate AI into their workflows.
However, this surge raises significant environmental concerns due to the substantial energy
and resource demands of AI technologies. With 460 TWh consumed in 2022, data centers
are responsible for 2% of the global energy consumption. This consumption is predicted
to be doubled by 2026, which can largely be attributed to the rise of AI technology.
This research aims to add to the discussion on sustainable AI by examining the use of
GenAI systems in businesses and providing actionable recommendations for developing
these systems, focusing on balancing productivity and sustainability within Arcadis. The
study employs a value-sensitive design approach to understand the ethical considerations
central to sustainable AI. Through interviews with key stakeholders, the study identifies
core values such as privacy, security, and sustainability that are vital in implementing AI
systems at Arcadis. The research further explores the conflict between sustainability and
productivity, examining model choice, cloud provider selection, and system architecture
as key factors influencing the environmental impact of AI. The study finds that while AI
tools can improve productivity, their deployment must align with ethical considerations
to support long-term sustainability. It recommends comprehensive lifecycle assessments
and emphasizes the importance of ethical alignment in AI development, focusing on
the desirability of the system. Moreover, choosing smaller, specific-purpose models
can significantly reduce energy consumption while maintaining adequate accuracy for
intended applications. Ultimately, the research advocates for prioritizing the ethical
integrity and societal benefits of AI systems over short-term business gains, ensuring that
AI contributes positively to both business performance and environmental sustainability.
Keywords: Artificial intelligence, Sustainable AI, Generative AI, Value-sensitive desig
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Faculteit der Sociale Wetenschappen