Will politics become a game of AI?

Cameron Conn

20 January 2022

Politics in recent years has been far more unpredictable and unstable than it was in the preceding period. Here are a few examples I’d like to focus on:

Russia’s annexation of Crimea. This resulted in the removal of Russia from the GB and the resumption of a pseudo war be tween The West and Russia.
Donald Trump’s presidential success. The messaging used by Trump to get elected was unlike any seen before, focusing on antagonising any perceived threat rather than policy.
The UK “Brexit” referendum. Both sides of the aisle often used dataless emotion to secure votes rather than proper argument. The UK has since had 3 Prime Ministers in 6 years.

These turbulent events happened in consecutive years (2014-2016), a statistically unlikely triad that was arguably the first of its kind since the Berlin wall demolishment to Soviet Union dissolution of 1989-1991.

A common view is that this political unrest was caused indirectly by the financial crash of 2008. The argument is that the crash created comprehensive discontent among the world’s population; driving people to desire radical alternatives and vote for the most extreme change no matter the cost. While I accept this in part, I believe the dominant factor is interconnected with the ascension of two converse phenomena:

How we use internet data
How the internet uses our data

The introduction of the iPhone in 2007 started a revolution in how we access internet data. Let’s take the UK as an example. In 2007, only 70% of the public had a home computer of any kind[1] and very few people had a smartphone. By 2011, 87% had a touch screen smartphone with internet connectivity [2]. Suddenly, the world was consuming data like never before. We were clicking, reading and watching our way through the day, rendering the 20th century view that a good memory is needed to be intelligent totally retrograde. Today, someone who can browse the web effectively can retrieve information at a rate which would put even the best eidetics to shame.

However, this utopia has its flaws. We are now completely at the mercy of the data we consume. Our opinions, policy preferences and general political views are of course a consequence of the articles, news broadcasts and social media posts we’ve seen. This unfortunately means that incorrect data or appeasing data (“clickbait”) can shape a population’s view beyond repair, resulting in political instability. The world was ready to be exploited.

Fortunately for the pessimist, humanity always finds a way to exploit where exploitation is possible. Enter, stage right of course, Steve Bannon, Lee Cain and Dominic Cummings. There are other examples, but in my opinion these 3 are the Svengalis of the data-driven political age.

Bannon was the CEO of Trump’s successful presidential campaign. He famously used Cambridge Analytica to measure the public mood from Facebook-likes and smartphone data, before targeting calculated swing voters with calculated swing messaging. Interestingly, Cambridge Analytica’s methods are “to a large de gree based on the academic work of Michal Kosinski”, released in 2008, just after the iPhone.

Cummings and Cain should prob ably be grouped together. They were the architects of the successful “Leave” campaign in the Brexit referendum and Boris Johnson’s rise to power. Cummings is attributed with the misleading claim that the UK was sending “the EU £350 million a week”. The data point delicately ignored any money sent back from the EU to the UK, but modelling showed that the public would respond positively to the claim, even if they knew it was misleading. The assumed reasoning is that to dispute the claim, you have to explain that the transfer of money between the UK and the EU is bidirectional, making them seem tightly linked, and evoking a nationalistic desire to break that link. This is only hypothesis though, and it is quite remarkable that a data driven approach can create political messaging with a known outcome, without understanding how it creates that outcome.

Cain is the messaging king. He proudly boasts on the website for his new company, Charlesbye, to have developed the famous slogans “Take Back Control”, “Back Boris” and “Get Brexit Done”. He uses similar meth ods to Cummings to develop his messaging, even advertising them as “data-driven” and explaining they are “memorable, powerful and authen tic”. Both Cummings and Cain regularly employed 3rd party companies to collect personal data and conduct surveys so they could develop their messaging. The majority of the data came from browser cookies, a method of tracking how people use the internet, what they look at, and how they respond.

As you can see, the work of our Svengalis not only exploits how we use internet data, but also my other salient point: how the internet uses our data (such as our Facebook-likes and cookies). The combination of the two has enabled background individuals to hold immense power over us, changing the course of major nations irreversibly.

My conclusion follows; I believe the dance between our data and the internet has directly lead to the political instability of recent years.

Who is to blame? It’s hard to say. The world has certainly taken to blaming the data fiends themselves, with Dominic Cummings and Steve Bannon in particular becoming villains in the eyes of many. However, is this just the media fighting back against what it views as “cheating”? After all, much of the media is de signed to influence, and techniques

honed over centuries seem to have been usurped by a few AI models and lots of data. Do we blame the devices we use? Certainly, without them much of this would be impossible, but at what cost? We could also turn against the software, such as cookies, or indeed software providers and data managers like Facebook. If the reader fancies a laugh I recommend watching “Zuckerberg explains the internet to Congress” on YouTube to see why it might be a while before politicians have the understanding to mount any reasonable attack.

Personally, I don’t think anyone in particular is to blame. The reality is that as time has passed, innovation has scaled exponentially and as a result regulations and society have struggled to keep up with the changing times. Some good changes are happening, such as Apple recently disabling IDFA (their cross application tracking technology) by default, but the outlook is definitely concerning. It may take major intervention to stop politics becoming a game of AI rather than a game of policy.

Sources:

[1] Thomas Alsop. Uk households: Ownership of home computers 1985-2018 , Dec 2020.

[2] S O’dea. Uk: Mobile phone ownership 1996-2018, Aug 2020.

DEUTSCHE

Die vierte industrielle Revolution, oder Industrie 4.0, ist gekennzeichnet durch das intelligente Vernetzen von Maschinen, Produkten und Mitarbeitern mit cyber-physikalischen Systemen in intelligenten Fabriken. Die Produktion in intelligenten Fabriken wird hochflexibel. Die Optimierung der Wertschöpfungskette erfolgt darin basierend auf Echtzeit-Anforderungen, Mitarbeiter werden von intelligenten Kontrollsystemen profitieren. In der intelligenten Fabrik wird die Kombination aus Material, Lieferanten, Ausrüstung und qualifizierten Mitarbeitern so organisiert, dass sie Einheiten produzieren, die den Kundenanforderungen in Echtzeit genügen. Die Personalisierung auf Kunden- und Marktsegmente wird durch integrierten Bedarfssignale gewährleistet, um skalierbare Geschäfte zu erzielen. Die intelligente Fabrik erfordert eine flexible und agile vernetzte Produktion.

Flexibilität bedeutet, dass die Betriebsmittel in der Lage sein müssen, eine Vielzahl unterschiedlicher Produktionsprozesse durchzuführen. Die Mitarbeiter müssen über ein sehr breites Spektrum an Qualifikationen und Erfahrungen im Umgang mit verschiedenen Systemen und Methoden verfügen.

Darüber hinaus erfordert die Flexibilität eine sehr hohe Verfügbarkeit der Einsatzmittel. Vernetzt bedeutet, dass alle relevanten Informationen für die Herstellung eines Produkts in Echtzeit zwischen allen Systemen und den am Prozess beteiligten Personen ausgetauscht werden. Tritt beispielsweise bei kritischen Anlagen ein Fehler auf, müssen neue Wege durch die Produktion gefunden werden, um Aufträge termingerecht zu erfüllen. Um das zu erzielen müssen alternative Arbeitspläne und die daraus resultierenden Produktionskosten und -zeiten analysiert werden. Neue Produktionseinheiten entstehen durch die neue Kombination von Ressourcen, Ausrüstung und Personal pro Auftrag. Dies erfordert eine enge Interaktion zwischen Ausrüstung, Ressourcen und Mitarbeitern mit einem intelligenten Produktionssystem, das sowohl die alternativen Routen als auch die Produktionskapazitäten verwaltet. Daher ist die Integration von Daten aus verschiedenen Systemen intern und extern unerlässlich.

Ziel dieses Forschungsprojekts ist es, Techniken zur Realisierung schlanker intelligenter Fabriken zu entwickeln, umzusetzen und zu validieren:

Das erste Projektziel ist die Entwicklung von neuen Algorithmen die den aktuellen Zustand der Fabrik bei der Erstellung des Produktionsplans berücksichtigen.

Das zweite Projektziel ist die Entwicklung einer Methodik für das Lösen von Systemen mit multiplen und teilweise probabilistischen Randbedingungen basierend auf reinforcement learning techniques.

Das dritte Projektziel ist es, eine mathematische Darstellung des genauen Zustands und Verhaltens der Produktionsanlagen zu generieren, die QiO den PARCS score nennt.

In diesem Arbeitspaket werden hochdimensionale teilweise diskrete Optimierungsprobleme mit einer Vielzahl von Nebenbedingungen mit modernen mathematischen Verfahren bearbeitet.

ENGLISH

The fourth industrial revolution, or Industry 4.0, is marked by the intelligent networking of machines, products and employees with cyber-physical systems in Smart Factories. Production in Smart Factories will be highly flexible, highly productive and resource efficient. The optimization of the value chain is based on real-time requirements. At the Smart Factory, the combination of materials, suppliers, equipment and skilled employees is organized to produce units that meet customer demands in real time. Personalization to customer and market segments is ensured by integrated demand signals to achieve scalable business.

The Smart Factory requires flexible and agile networked production. Flexibility means that the operating resources must be able to carry out a variety of different production processes. Employees are required to have a very broad range of qualifications and experience in using different systems and methods. Furthermore, flexibility requires a very high availability of operating resources.

Networked means that all the relevant information for the manufacture of a product is shared in real-time between all of the systems and people involved in the process. This requires information about potential machine breakdown, delays to a supplier order, missing parts, personnel availability or available skills.

If a fault occurs to critical piece of equipment, for instance, new routes through production must be found in order to fulfil orders on time. In order to do this, alternative work schedules and the resulting production costs and times must be analysed. New production units arise through the new combination of resources, equipment and personnel per order. This requires close interaction between equipment, resources and the workforce with a master intelligent production system, which manages the alternative routes as well as production capacity.

As such integration of data from diverse systems internal and external is therefore essential. The aim of this research project is to develop, implement and validate techniques for the realization of Smart Factories:

The first project objective is the development of new algorithms that take into account the current state of the factory when creating the production plan.

The second project objective is the development of a methodology for solving systems with multiple and partially probabilistic constraints based on reinforcement learning techniques.

The third goal of the project is to generate a mathematical representation of the exact condition and behaviour of the production facilities, which QiO calls the PARCS™ score. It is designed to enable engineers within software tools such as the QiO Foresight Engine™.

This work package deals with high-dimensional, partially discrete optimization problems with a variety of constraints using modern mathematical methods. The results of the project proposed here go well beyond the general state of the art, especially in the fields:

Artificial intelligence for the dynamic learning and modeling of production processes

Artificial intelligence for production planning and supply chain management

Traditional approaches to modeling and describing production processes assume a well-known problem and seek a solution to a specific problem. This approach is not suitable for solving such problems in the context of an intelligent factory. The boundary conditions that describe the processes change regularly here and they are mostly of a probabilistic nature. We plan to learn the processes dynamically based on the data produced by a production hall. We call such dat, the voice of the production hall.

Such automated learning is important because constructing a graphical model by a human expert can be cumbersome and time-consuming. If, as is often the case, there is a database of examples, learning techniques can do at least part of the work. This approach should lead to an always current and dynamic view on the production processes. It should also form a mathematical basis for dynamic production planning as it will be described in the next.