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– Autonomy now

Making sense of a changing world

It’s difficult to make sense of a fast changing world. But could ‘autonomy’ be at the centre of it? I’ll explain.

There are two main themes – people and technology. Ideas about autonomy are changing for both. For people, it is a matter of their relationship to employment, government and the many institutions of society. For technology, it is the introduction of autonomous intelligence in a wide range of systems including phone apps and all manner of automated decision making systems that affect every aspect of our lives. There is also the increasing inter-dependency between people and technology, both empowering and constraining. Questions of autonomy are at the heart of these changes.

There have been times in history when it has not occurred to people that they could be autonomous in the broad scope of their lives. They were born into a time and place where the control of their destiny was not their own concern. They were conditioned to know their place, accept it and stay in it. In first world democracies, autonomy is, perhaps, a luxury of the here and now. It may not necessarily stay that way.


My particular interest is the way in which we are giving autonomy to the things that we create – computer algorithms, artificial intelligence systems and robots. But it’s broader than that. We all want the freedom to pursue our own goals, to self-determine. We are told repeatedly by an industry concerned with self-development and achieving success, that we should ‘find our authentic self’ and pursue the values and goals that really matter to us.

However, we can only do this within an environment of constraints – physical constraints, resource constraints, psychological constraints and social constraints. It is the dynamic between the individual and their constraints that is in constant flux and that I am trying to examine.

What’s Trending in Autonomy?

There are two main trends – one towards decentralisation and one towards concentrations of wealth and power. This seems something of a contradiction. How can both be true and, if so, where is this going?

There is a long-term trend towards decentralization. First we rejected the ancient gods as the controllers of nature. Much more recently we have started to question other sources of authority – governments, doctors, the church, the law, the mainstream media and many other institutions in society. As we as individuals have become more informed and empowered by technology, we have started to see the flaws in these ‘authorities’.

I believe, along with many other commentators, that we are heading towards a world where autonomy is not just highly valued but is also more possible than it ever has been. As society becomes better educated, as technology enables greater information sharing and flexibility, we can, and perhaps inevitably will, move towards a more decentralized society in which both human and artificial autonomous agents increasingly interact with each other. The interactions will become more frequent, more informed, more fine-grained and more local. The technological infrastructure for this continues to rollout at ever-increasing pace. Soon we will have 5G communications facilitating almost instantaneous communications between ever more intelligent and powerful devices – smart phones, autonomous vehicles, information servers, and a host of smart devices.

On the other hand, there is clear evidence of increased concentrations of wealth and power. Although estimates may vary, it seems that a greater proportion of the worlds wealth is held by fewer and fewer people. Stories abound of fewer that eight people owning more than half the worlds wealth. Economists like Thomas Pickerty have documented in detail the evidence for such a trend.

There is clearly a tension between these trends. As power and wealth become more concentrated, manifesting in the form of surveillance capitalism (not ignoring surveillance by the state) and fake news, there is a fight back by individuals and other institutions.

Individuals increasingly recognise the intrusions on their privacy and this is picked up (often belatedly) in legislation like GDPR and other moves to regulate. The checks and balances do work to modulate the dynamics of the struggle, but when they don’t work, the accumulated frustration at the loss of human dignity can become political and violent. Take a closer look at autonomy.

Why do we need Autonomy?

We each have a biological imperative to survive. While we can count on others to some extent, ‘the buck stops’ with each of us as individuals. The more robust and resilient solutions are self-sufficiency and self-determination. It’s not a fail-safe but it takes out the risk that others might not be there for us all the time. It also appears to be a route to greater wellbeing. Learning, developing competence and mastery, being able to predict and hence increase the possibility that we can control, being less subject to constraints on our actions – all contribute to satisfaction, ultimately in the service of survival.

In the hierarchy of needs, having enough food, shelter, sleep and security releases some of your own resources. It provides the freedom to climb. Somewhere near the top of the hierarchy is what Maslow called self-actualisation – the discovery and expression of your authentic self. But unless you are exceptionally lucky, and find that your circumstances align perfectly with your authentic self, then a pre-requisite is to have freedom from the constraints that prevent you from getting there.

Interactions between people and machines

This is all the human side of autonomy – the bit that applies to us all. This is a world in which both people and artificial agents – computer algorithms, smart devices, robots etc. interact with each other. Interactions between people and people, machines and machines and people and machines are accelerating in both speed and frequency in order that each autonomous agent can achieve its own rapidly changing priorities and goals. There is nothing static or certain in this world. It is changing, ambiguous, and unpredictable.

Different autonomous agents have different goals and different value systems that drive them. For people these are different cultures, social norms and roles. For machines they relate to their different functions and circumstances in which they operate. For interactions to work smoothly there needs to be some stability in the protocols that regulate them. Autonomy may be a way into defining accountability and responsibility. It may lead us towards mechanisms for the justification and explanation of action. Neither machines nor people are very good at this, but autonomy may provide the key that unlocks our understanding of effective communication and protocols.

Still, that’s for later. Right now, this article is just focused on the concept of autonomy.
I hope you are convinced that this is an important and interesting subject. It is at the foundation of our relationships with each other and between people and the increasingly autonomous and intelligent agents we are creating.

Questions that need to be addressed


  • What do we mean by autonomy?
  • How do agents (people and machines) differ in the amount of autonomy they have?
  • Can we measure autonomy?
  • What examples of peoples societies and artefacts can we think of that might help us understand what is it what is not autonomous?
  • What do we mean by autonomy when we talk about artificial autonomous intelligence systems?
  • Are the computer algorithms and robotic systems that we have today truly autonomous?
  • What would it mean to build an artificial intelligence that was truly autonomous?
  • What is the relationship between autonomy and morality?
  • Can we be truly autonomous if we are constrained by ethical principles and social norms?
  • If we want our intelligent artefacts to behave ethically, then how would we approach the design of those systems?

That’s quite a chunk of questions to get through. But they are all on the critical path to understanding how our own human autonomy and the autonomy that we build into artefacts, can relate to and engage with each other. They take us to a point where we can better understand the trade-offs every intelligent agent, be it human or artificial, has to make between the freedom to pursue its own goals and the constraints of living in a society of other intelligent agents.

It also reveals how, in people, the constraints of society are internalised. As adults they have become part of our internal control mechanisms. These internal controls have no absolute morality but reflect the circumstances and society in which we grow up. As our artefacts become increasingly intelligent we may need to develop similar mechanisms for their socialisation.

Definitions of Autonomy

The following definitions are taken from the glossary of the IEEE publication called ‘Ethically Aligned Design’ (version 1). The glossary has several definitions from different perspectives:

Ordinary language: The ability of a person or artefact to govern itself including formation of intentions, goals, motivations, plans of action, and execution of those plans, with or without the assistance of other persons or systems.

Engineering: “Where an agent acts autonomously, it is not possible to hold any one else responsible for its actions. In so far as the agent’s actions were its own and stemmed from its own ends, others cannot be held responsible for them” (Sparrow 2007, 63).

Government: “we define local [government] autonomy conceptually as a system of local government in which local government units have an important role to play in the economy and the intergovernmental system, have discretion in determining what they will do without undue constraint from higher levels of government, and have the means or capacity to do so” (Wolman et al 2008, 4-5).

Ethics and Philosophy: “Put most simply, to be autonomous is to be one’s own person, to be directed by considerations, desires, conditions, and characteristics that are not simply imposed externally upon one, but are part of what can somehow be considered one’s authentic self” (Christman 2015).

Medical: “Two conditions are ordinarily required before a decision can be regarded as autonomous. The individual has to have the relevant internal capacities for self-government and has to be free from external constraints. In a medical context a decision is ordinarily regarded as autonomous where the individual has the capacity to make the relevant decision, has sufficient information to make the decision and does so voluntarily” (British Medical Association 2016).

More on autonomy later. Sign up to the blog if you want to be notified.

Meanwhile a couple of videos

The first has an interesting take on autonomy. Autonomy is not a matter of what you want, but what you want to want. The more reflective you are about what you want the more autonomous you are.

Youtube Video, What is Autonomy? (Personal and Political), Carneades.org, December 2018, 6:50 minutes

https://www.youtube.com/watch?v=z0uylpfirfM

The second is from a relatively new Youtube channel called ‘Rebel Wisdom’. It starts with the breakdown of trust in traditional media and moves on to themes of decentralisation.

Youtube Video, The War on Sensemaking, Daniel Schmachtenberger, Rebel Wisdom, August 2019, 1:48:49 hours

https://www.youtube.com/watch?v=7LqaotiGWjQ&t=17s


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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. It will have 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.

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.

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