As described in previous posts, governance has evolved historically, assuming different configurations depending on socio-economic and political contexts. In the Capitalist era, the nation-state emerged as the governance master. Still, alternative governance processes demanding the involvement of non-state actors have challenged it in the past 40 years or so. That has put pressure on existing governance structures, which are now lagging behind widespread changes in governance processes, trying to quickly cater to the new demands and realities. That said, it should be clear by now that governance has its own internal dynamics and thus behaves like a moving target. In this context, its four core elements (social group, structures, processes, and outcomes) constantly flow in two distinct ways. First, the nature of each change historically. And second, the interactions among them are also in flux. Studying both is thus critical to fully understanding governance systems.
In this light, AI governance can be considered as a particular governance case, in the same fashion we study corporate or economic governance, or IT governance, for that matter. I previously examined the relationship between AI, IT and ICTs and concluded that, while AI is part of ICTs, it also has the potential to change how ICTs and older AI technologies work. AI thus has a seemingly magic transformational wand that cannot be ignored when deployed in any scenario. But such magic goes well beyond the ICT kingdom. According to many researchers and analysts, AI is indeed a general-purpose technology that will profoundly impact most, if not all, sectors of society. It thus has almost universal applicability, pretty much like governance, as I have defined it.
From a pure governance perspective, AI governance has the same four components as any other sectoral or domain-based governance system type. Indeed, we see social groups (usually nation-states) designing and deploying institutional or organizational structures supported by specific processes to achieve a previously agreed outcome. The latter is typically framed within “ethical” or “responsible” AI deployments. But other options are also available, including inclusive AI or AI to support the achievement of the SDGs and its multiplicity of targets, for example. As mentioned, setting such outcomes is also a governance matter and is typically contentious, as power structures and uneven power distribution among stakeholders play a crucial role. That, however, is a common trait of most governance processes and is thus not unique to AI.
Unique to AI is its magic transformation wand, which can also impact governance systems. The AI in governance concept, which has been around for a while, captures such a potential impact. One of the issues at stake here is that, in practice, AI governance has mostly ignored the potential impact of AI in governance. In fact, in many AI deployments, it is indeed considered a technical AI feature that humans should not tamper with, thus leading to well-known nefarious human outcomes. Here, AI’s magic wand becomes reified, with AI impersonating humans backed by sophisticated software that cannot make any mistakes. The AI is always right!
The impact of AI on governance systems, old and new, depends on the type of AI we are dealing with. Generally, the more sophisticated the AI platform, the more significant its potential governance impact. In that light, Deep Learning algorithms and GenAI pose the most critical governance challenges. Unlike ICTs and older AIs, new AI technologies are more prone to process, analyze, summarize, and generate knowledge that can become an integral part of existing or new governance processes while simultaneously challenging existing governance structures incapable of adequately handling new dynamics. Such integration can take place in two different forms. One is almost automatic and lets the AI platform directly contribute to the governance process. The second demands the mediation of governance structures to assess the AI inputs and, on that basis, decide its contribution levels.
The governance of AI governance is thus essential. And it should not ignore the tight relationship between the governance of AI and AI in governance. These are not two different beasts but two sides of the same coin that interact dynamically. However, nothing prevents any social group from using one side of the coin while ignoring the other. But that can have high social and human costs.
The figure below summarizes the overall governance of AI governance processes.
Given a particular social outcome, a governance structure must be in place to assess the feasibility of deploying AI. That entails governance processes that, in principle, should allow participation from stakeholders and potential beneficiaries. Identifying the most adequate platform and agreeing on its deployment are crucial steps here (1). However, before implementation commences, the governance structure should also consider the potential impact the chosen AI could have on governance (2). It should then agree on ways to handle its deployment (3), which can end transformation or impact the overall governance structure, as well as all those involved in complementary processing and actual implementation (4).
In this fashion, the only way something like algorithmic governance could take place is if the governance structures and processes agree that such a state of affairs is ethical, responsible, inclusive, or developmental. That might be harder to implement if the assessment of AI’s impact on governance is carried out openly and transparently. In any event, redressing and revisiting mechanisms should also be part and parcel of any AI governance process.
Raul
