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Abstract
Technology is a product of society. As technology evolves, the norms governing it have to mature for enabling its proper use within the society. The interest in Artificial Intelligence (AI) has surged following the introduction of chatGPT. Firms, both large and small, are competing to develop new products and solutions involving Al. Amidst these developments, leading corporations such as Google and Microsoft have proactively committed to upholding responsible innovation in AI development. Governments worldwide are responding with the creation of guidelines and regulations in the field. Notably, on March 21, 2024 the United Nations General Assembly (UNGA) adopted landmark regulation on AI.
At the heart of these developments in AI are engineering managers who leverage technical advances to build products and services that create value. To effectively harness AI for human benefit, engineering managers must be aware of these evolving regulations governing Al. Some regulations such as Digital Markets Act (DMA) and General Data Protection Regulations (GDPR) have far reaching consequences for organizations globally. Having a working knowledge of these statutory requirements will enable engineering managers to identify the opportunities and constraints in leveraging AI technology while building products and services. It will allow them to make informed decisions about data collection methods, model training processes, the deployment of AI systems and metrics for their evaluation. At scale, it can become a competitive advantage for the firms they work in, as explored through real-world examples in this paper.
Keywords
AI regulations, responsible AI, human centered design, fairness, interpretability, privacy, safety,
Introduction
The advent of increasingly sophisticated AI models, exemplified by OpenAl's ChatGPT launched in Nov 2022, has propelled AI into the mainstream. It has sparked a surge of interest and investment in Al-powered applications across diverse sectors (Al investment, 2023). Notable feature of this phase of AI research is that it is predominantly industry led. In 2023 alone, industry produced 51 notable machine learning models, while academia contributed only 15. (Maslej, et al., 2024). There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high. (Maslej, et al., 2024). Dr. Andrew Ng, a prominent computer scientist and Coursera co-founder, compared AI to electricity in its significance, underscoring its vast potential. (Ng, 2017),
AI development covers such broad scope and...