What if we asked an AI to cure cancer and it eliminated cancer by eliminating humans? This thought experiment, though extreme, is often used to highlight the risks of misaligned AI systems and the unintended consequences of poorly framed objectives that make the possible implications of AI in society very real. It also reveals a deep challenge.
We are entering what many believe could be the most consequential decade of human history. The choices we make and the decisions that are made (or not made) about AI are going to create the future. Across academia, disciplines are rapidly incorporating AI and its potential capabilities into research and innovation, as well as teaching curricula. Some focus predominantly on the opportunities, others on the risks. Yet, despite good intentions and much talk of interdisciplinary thinking that is needed to address a rapidly changing complex world, academic silos continue to exist.
As we develop and design AI we have to tackle this challenge that is hiding in plain sight; disciplinary silos are hindering a holistic understanding of AI and responsible innovation.
Why disciplinary siloes in AI matter more than we think
Disciplinary siloes create the opportunity for disciplinary blindness. This occurs when individual disciplines can only achieve a partial view of risks. A discipline can address risk in one area but be blind to the associated and confounding risks considered in another discipline. For example, medicine and climate science both address AI. In medicine, AI is accelerating breakthroughs in diagnostics, drug and vaccine discovery and laboratory testing, which is bringing insight that has the potential to improve and save millions of lives globally. At the same time, in climate science, the environmental impact of AI and its expansion is being addressed with a focus on carbon dioxide emissions, water and electricity consumption. While the advances in medicine have the potential to save millions of lives, the consequences of the environmental impact may impact billions.
A second challenge arises from disciplines having different and contradictory notions of risk and what constitutes desirability. AI, for example, can automate repetitive tasks highly effectively. In some disciplines, such as education, its potential for rapid marking and feedback creates the opportunity to free up educators to address aspects of teaching that require creativity and nuance in complexity. In a similar way, in the creative industries, production is being transformed by AI tools that accelerate and scale certain tasks. Yet, it is possible to argue that these repetitive tasks that are being outsourced and automated are a key source of human creativity and expertise, which is achieved through trial and error, iteration and reflection. Herein lies the contradiction; the very capability that is progressing one discipline may also be undermining another. Losing the iterative repetitive tasks may remove the path to developing creativity and expertise.
Finally, even within the same discipline, contradictions become apparent. Climate science illustrates this with the potential for AI to enable enhanced environmental governance and aid the development of solutions to mitigate the effects of climate change and promote long-term sustainability. At the same time, as we have already seen, there is also a focus within the discipline that is addressing the strain on environmental systems caused by the increase in the use of AI tools