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– AI in Politics 2026

By in Accountability, Transparency on .

AI in Politics – Jan26 – Download .pdf here

Artificial Intelligence in Politics

AI has the potential to change politics and policy making. In fact, it is already doing this. Look at the way social media is using AI to influence election outcomes. However, we need to recruit AI to improve political processes, not seize control of them.

If we do not take stock of this now, there is a real danger that the important decisions influencing UK society will be increasingly made by people and organisations outside the UK political arena. Decision-making processes will become more hidden from public view and the opportunity for debate, scrutiny and accountability will be lost.

In contrast there are many ways in which AI can be used to enhance democratic processes. In the short term, it can enable better civil engagement, aid policy insights and forecasting, increase administrative efficiency and make political processes more transparent. In the longer term it can lead to constitutional innovation, facilitate deliberative democracy, make explicit the trade-offs between the interests of different groups, critique proposed legislation from multiple perspectives, co-ordinate central and local plans, and generally augment government decision-making at all levels.

However, changes to the processes of political and policy decision-making are hugely difficult. Our current systems are deeply embedded and entangled. Habit, complexity and vested interests are intimately related barriers to any change and especially so in relation to political decision-making itself. And often rightly so, because processes that have stood the test of time are important to keep and not disrupt. But AI has come out of left field as a major disruptor to almost everything and its implication for political decision making must be assessed and absorbed. To achieve this, we need to not just focus on how AI can be used to enhance the economy but also how AI can be used to enhance the political decision-making processes themselves.

We are in danger of rolling out AI such that it merely automates our current ways of going about things, embedding some poor practices even further. However, abrupt and radical change is also unacceptable because it is disruptive and de-stabilizing. What is needed is a firm and agreed vision based on sound principles, and a plan of managed transition that works towards new ways of making political decisions incorporating AI, that are based on best practice (e.g. evidence driven, comprehensively consulted, robustly modelled and tested etc.).

This brief paper is a call for us to take stock of what has already been done, both in the research and practice, in using AI for political and policy formulation, decision-making, implementation, monitoring and iterative revision. This sets out the landscape so that we can better plan the journey ahead and help head-off a situation where players outside parliament (large corporations, foreign governments, the media etc.) use their increasingly powerful command of AI to determine the political agenda.

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How do we embed ethical self-regulation into Artificial Intelligent Systems (AISs)? One answer is to design architectures for AISs that are based on ‘the Human Operating System’ (HOS).

Theory of Knowledge

A computer program, or machine learning algorithm, may be excellent at what it does, even super-human, but it knows almost nothing about the world outside its narrow silo of capability. Even Large Language Models (LLM) with their impressive ability to respond to prompts really only know about the statistical relationships between words.  They do not really ‘understand’ what they say.  An LLM has little or no capacity to reflect upon what it knows or the boundaries of its applicability. This ‘meta-knowledge’ may be in the heads of their designers but even the most successful AI systems today can do little more than what they are designed to do. Even in 2026 will are some way from Artificial Genereral Intelligence (AGI).

Any sophisticated artificial intelligence, if it is to apply ethical principles appropriately, will need to be based on a far more elaborate theory of knowledge (epistemology).

The epistemological view taken in this blog is eclectic, constructivist and pragmatic. It attempts to identify how people acquire and use knowledge to act with the broadly based intelligence that current artificial intelligence systems lack.

As we interact with the world, we each individually experience patterns, receive feedback, make distinctions, learn to reflect, and make and test hypotheses. The distinctions we make become the default constructs through which we interpret the world and the labels we use to analyse, describe, reason about and communicate. Our beliefs are propositions expressed in terms of these learned distinctions and are validated via a variety of mechanisms, that themselves develop over time and can change in response to circumstances.

Reconciling Contradictions

We are confronted with a constant stream of contradictions between ‘evidence’ obtained from different sources – from our senses, from other people, our feelings, our reasoning and so on. These surprise us as they conflict with default interpretations. When the contradictions matter, (e.g. when they are glaringly obvious, interfere with our intent, or create dilemmas with respect to some decision), we are motivated to achieve consistency. This we call ‘making sense of the world’, ‘seeking meaning’ or ‘agreeing’ (in the case of establishing consistency with others). We use many different mechanisms for dealing with inconsistencies – including testing hypotheses, reasoning, intuition and emotion, ignoring and denying.

Belief Systems

In our own reflections and in interactions with others, we are constantly constructing mini-belief systems (i.e. stories that help orientate, predict and explain to ourselves and others). These mini-belief systems are shaped and modulated by our values (i.e. beliefs about what is good and bad) and are generally constructed as mechanisms for achieving our current intentions and future intentions. These in turn affect how we act on the world.

Human Operating System

Understanding how we form expectations; identify anomalies between expectations and current interpretations; generate, prioritise and generally manage intentions; create models to predict and evaluate the consequences of actions; manage attention and other limited cognitive resources; and integrate knowledge from intuition, reason, emotion, imagination and other people is the subject matter of the human operating system.  This goes well beyond the current paradigms  of machine learning and takes us on a path to the seamless integration of human and artificial intelligence.