[New essay] ‘Monopolisation - Concentrated power and economic embeddings in Machine Learning & Artificial Intelligence’

US and EU regulators are focusing on the market power and monopolistic behaviour of big tech firms. However, they tend to focus mostly on the conduct and market position of online platforms, while ignoring the underlying technologies by which they operate. But what if techniques like machine learning (ML) are themselves factors in the continuous expansion of already oversized tech giants? 

In a new essay on the A New AI Lexicon project (AI Now Institute and Data & Society Research Institute), Asser senior researcher Geoff Gordon and co-authors Bernhard Rieder (UvA), Giovanni Sileno (UvA) point to material aspects of technologies for machine learning (ML) and Artificial Intelligence (AI) as often-overlooked factors in the continuous expansion of ‘already oversized tech giants’.

In the essay, the authors stress that the technologies behind these platforms ‘may further exacerbate the trend towards monopolisation in the tech sector and the problems that come with it’. The authors argue that the critical debate about potential social harms of AI needs to be mindful of broader societal stakes:  

“Traditionally, monopolies are seen as problematic because they may lead to rising prices for consumers, diminished product quality, or negative repercussions on labour conditions. While these things may not necessarily hold true for tech companies offering many ‘free’ products, critics have argued that the new ‘data-opolies can actually be more dangerous than traditional monopolies,’ because they ‘affect not only our wallets but our privacy, autonomy, democracy, and well-being’”.

Dominating research output
Large tech firms like Amazon, Facebook, Google, and Microsoft in the US and Alibaba, Baidu, and Tencent in China, have moved to ‘corner the market’ for expertise in ML and AI. According to the authors, these companies now dominate research output in computer science, particularly around deep learning. This leads to a process of ‘de-democratization’ in knowledge production, and companies such as Open AI becoming ‘de facto arbiter of ethics and morality with regard to the deployment of AI services’.

Over the last years, the most powerful players in AI have also pursued an aggressive acquisition strategy. In the AI and ML space, a small handful of massive firms continuously buy up new participants as they emerge. While in other fields that feature regular mergers – such as banking and insurance, medical services, telecommunications, media – market participants are already regulated, the authors write, this is not equally the case for the AI and ML industry.

Concentrated control
According to the authors, the risks of monopolisation and concentrated control over technological capabilities are varied. Apart from concerns about privacy and bias, and the effects on democratic agency and individual autonomy, “(…) the reliance on a small set of actors can create relations of dependency that may not only translate into economic and political pressure, but also prove more costly in the long run whether or not these actors decide to raise prices after all. Furthermore, AI components are becoming increasingly important in public and private service provisions, in embedded devices, in critical infrastructures, and elsewhere. Concentrated control over the ‘means of production’ - the means to build both applications relying on AI technologies and the AI technologies themselves - guarantees wide-ranging influence on the specific workings of these components as well as the overall development of AI as a technological field.”

Considering that most of the dominant players in the field are currently located in the US and China, the authors stress that “the geopolitical stakes around concentrated control over technological capabilities cannot be ignored.”

Read the full essay.

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The essay Monopolisation - Concentrated power and economic embeddings in ML & AI is part of the ongoing “AI Lexicon” project, by the AI Now Institute and Data & Society Research Institute (New York University).