Why Computer Science and Humanities Should Shape AI Future
The first quarter of the 21st century has seen enormous technological change, especially in the areas of machine learning (ML) and artificial intelligence (AI) with numerous and varied societal consequences. Recommender systems now mediate many of our social interactions – whether for business, entertainment, news, or personal relationships.
These systems are driven by ML algorithms trained on the big data made available only with the user-generated content of Web 2.0 in the 2000s – and with the parallel computing capacity simultaneously brought on through repurposing graphics processing units (GPUs). In the 2010s, deep learning produced significant improvements on (e.g. image) classification tasks; and the transformer architecture was first applied in the construction of large language models (LLMs) for use in machine translation.
Meanwhile, there was progress in generative AI, and the 2020s saw the influential launch of both image generators and general purpose LLM-powered chatbots. Advances in AI have even reached into the most cherished and distinctive areas of human endeavour: in 2022, an AI generated artwork won first prize in the fine art competition of the Colorado state fair; and in 2024, the Nobel Prize for Chemistry went to scientific researchers at DeepMind for AlphaFold, an AI model used in protein structure discovery. And society is rushing to keep up: the OECD, for example, is recommending urgent action from policy makers to address the transformation in the labour market that is likely to follow; meanwhile the EU has enacted the world’s first regulation targeting the use of AI.
What will the next quarter century bring? Some envision techno-utopias in which major civilizational problems such as war, poverty, and climate change will be addressed through advances made possible by the advent of artificial general intelligence (AGI); they therefore advocate ‘effective accelerationism’ to bring about this positive transformation as quickly as possible. Others are moved instead by concerns about intrusions on privacy and growing inequality fuelled by ever wider data collection (e.g. via the internet of things) and the commercially proprietary AI systems that use it – they imagine more dystopian futures (such as that depicted in Neill Blomkamp’s 2013 film, Elysium, starring Matt Damon, in which the elite live on a satellite with easy access to the key ingredients of human wellbeing, such as healthcare, while the majority are subjected to surveillance on a resource depleted Earth). Less extreme projections also come in both optimistic and pessimistic flavours, respectively emphasizing the opportunities afforded by near-term advances in AI, or the risks associated with the reckless development and adoption of unsafe and disruptive technologies.
What seems reasonably clear is that, in order to shape our socio-technical future, insights will be needed from both computer science and engineering on the one hand, and the arts, humanities, and social sciences on the other. Only this combination of perspectives will encompass the technical knowledge of what is feasible and how it can be developed, as well as the deep understanding of what is humanly possible and desirable, to steer incremental change for the better. Indeed, researchers at Northeastern University’s Network Science Institute and the Institute for Experiential AI are now calling for a new field of study, investigating the interaction, even coevolution, of humans and AI (see here for further details). And at the University’s London campus, undergraduate and graduate students are involved in both computational and philosophical research surrounding AI art, ethics, science, and linguistic competencies, as well as the societal impacts, including polarization and factionalization of AI generated or facilitated mis and disinformation on scientific communities and public opinion. The knowledge produced by these and related investigations will inform the actions we are able to take, both individually and collectively, in ensuring the development and deployment of the next generation of technologies in ways that are beneficial, respectful, and just.
About the author
Brian Ball is a Professor of Philosophy at Northeastern University London. A member of the 695th Lord Mayor of London’s Ethical AI Initiative Steering Group, his research in computational philosophy has been funded by the British Academy, the Royal Society, and the Leverhulme Trust. He is a co-author of ‘Training Philosopher Engineers for Better AI’ (2022) and co-editor of the two volume collection Wittgenstein and Artificial Intelligence (2024).
This sponsored article has been produced with Northeastern University London.
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