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Cambridge (UK) is awash with talks at the moment, and many of these are about artificial intelligence. On Tuesday (12th of March 2019) I went to a talk, as part of Cambridge University’s science festival, by José Hernández-Orallo (Universitat Politècnica de València), titled Natural or 'Artificial Intelligence? Measures, Maps and Taxonomies'.
José opened by pointing out that artificial intelligence was not a subset of human intelligence. Rather, it overlaps with it. After all, some artificial intelligence already far exceeds human intelligence in narrow domains such as playing games (Go, Chess etc.) and some identification tasks (e.g. face recognition). But, of course, human intelligence far outstrips artificial intelligence in its breadth and the amount of training needed to learn concepts.
José‘s main message was how, when it comes to understanding artificial intelligence, we (like the political scene in Britain at the moment) are in uncharted territory. We have no measures by which we can compare artificial and human intelligence or to determine the pace of progress in artificial intelligence. We have no maps that enable us to navigate around the space of artificial intelligence offerings (for example, which offerings might be ethical and which might be potentially harmful). And lastly, we have no taxonomies to classify approaches or examples of artificial intelligence.
Whilst there are many competitions and benchmarks for particular artificial intelligence tasks (such as answering quiz questions or more generally reinforcement learning), there is no overall, widely used classification scheme.
My own take on this is to suggest a number of approaches that might be considered. Coming from a psychology and psychometric testing background, I am aware of the huge number of psychological testing instruments for both intelligence and many other psychological traits. See for example, Wikipedia or the British Psychological Society list of test publishers. What is interesting is that, I would guess, most software applications that claim to use artificial intelligence would fail miserably on human intelligence tests, especially tests of emotional and social intelligence. At the same time they might score at superhuman levels with respect to some very narrow capabilities. This illustrates just how far away we are from the idea of the singularity - the point at which artificial intelligence might overtake human intelligence.
Another take on this would be to look at skills. Interestingly, systems like the Amazon's Alexa describe the applications or modules that developers offer as 'skills'. So for example, a skill might be to book a hotel or to select a particular genre of music. This approach defines intelligence as the ability to effectively perform some task. However, by any standard, the skill offered by a typical Alexa 'skill', Google Home or Siri interaction is laughably unintelligent. The artificial intelligence is all in the speech recognition, and to some extent the speech production side. Very little of it is concerned with the domain knowledge. Even so, a skills based approach to measurement, mapping and taxonomy might be a useful way forward.
When it comes to Ethics, There are also some pointers to useful measures, maps and taxonomies. For example the blog post describing Josephine Young’s work identifies a number of themes in AI and data ethics. Also, the video featuring Dr Michael Wilby on the http://www.robotethics.co.uk/robot-ethics-video-links/ page starts with a taxonomy of ethics and then maps artificial intelligence into this framework.
But, overall, I would agree with José that there is not a great deal of work in this important area and that it is ripe for further research. If you are aware of any relevant research then please get in touch.
We are all deluded. And for the most part we don’t know it. We often feel as though we have control over our own decisions and destiny, but how true is it? It’s a bit like what US Secretary of Defence, Donald Rumsfeld, famously said in February 2002 about the ‘known knowns’, the ‘known unknowns’ and the ‘unknown unknowns’.
Youtube video, Donald Rumsfeld Unknown Unknowns !, Ali, August 2009, 34 seconds
The significance for ROBOT ETHICS: If people can only act on the basis of what they know, then it is easy to see the implications for artificial Autonomous Intelligent Agents (A/ISs) like robots, that ‘know’ so much less. They may act with the same confidence as people, who have a bias to thinking that what they know and their interpretation of the world, is the only way to see it. Understanding the ‘goggles’ through which people see the world, how they learn, how they classify, how they form concepts and how they validate and communicate knowledge is fundamental to embedding ethical self-regulation into A/ISs.
How can a brain that is deluded even get an inkling that it is? For the most part, the individual finds it very difficult. Interestingly, it is often those who are most confident that they are right who are most wrong (and dangerously, who we most trust). The 2002 Nobel Prize winner, Daniel Kahneman has spent a lifetime studying the systematic biases in our thinking. Here is what he says about confidence:
Youtube video, Daniel Kahneman: The Trouble with Confidence, Big Think, February 2012, 2:56 minutes
The fact is, that when it comes to our own interpretations of the world, there is very little that either you or I can absolutely know as demonstrated by René Descartes in 1637. It has long been know that we have deficiencies in our abilities to understand and interpret the world, and indeed, it can be argued that the whole system of education is motivated by the need to help individuals make more informed and more rational decisions (although it can be equally argued that education and training in particular, is a sausage factory in the service of employers whose interests may not align with those of the individual).
The significance for ROBOT ETHICS: Whilst people may have some idea that there are things they do not know, this is generally untrue of most computer programs. Young children start to develop ethical ideas (e.g. a sense of fairness) from an early age. Then it takes years of schooling and good parenting to get to the point where, as an adult, the law assumes you have full responsibility for your actions. This highlights the huge gap between an adult human’s understanding of ethics and what A/ISs are likely to understand for the foreseeable future.
The debate about whether we should act by reason or by our intuitions and emotions is not new. The classic work on this is Kant’s ‘Critique of Pure Reason’ published in 1781. This is a masterpiece of epistemological analysis covering science, mathematics, the psychology of mind and belief based on faith and emotion. Kant distinguishes between truth by definition, truth by inference and truth by faith, setting out the main strands of debate for centuries to come. Here is a short, clear presentation of this work.
Introduction to Kant’s Critique of Pure Reason (Part 1 of 4), teach philosophy, September 2013, 4:52 minutes
From an individual’s point of view, by a process of cross validation between different sources of evidence (people we trust, the media and society generally, our own reasoned thinking, sometimes scientific research and our feelings), we are continuously challenged to construct a consistent view about the world and about ourselves. We feel a need to create at least some kind of semi-coherent account. It’s a primary mechanism of reducing anxiety. It keeps us orientated and safe. We need to account for it personally, and in this sense we are all ‘personal’ scientists, sifting the evidence and coming to our own conclusions. We also need to account for it as a society, which is why we engage in science and research to build a robust body of knowledge to guide us.
George Kelly, in 1955, set out ‘personal construct theory’ to describe this from the perspective of the individual – see, for example this straight-forward account of constructivism which also, interestingly, proposes how to reconcile it with Christianity – a belief system based on an entirely different premise, methodology and pedigree):
But for the most part there are inconsistencies – between what we thought would happen and what actually did happen, between how we felt and how we thought, between how we thought and what we did, between how we thought somebody would react and how they did react, between our theories about the world and the evidence. Some of the time things are pretty well what we expect but almost as frequently, things don’t hang together, they just don’t add up. This drives us on a continuous search for patterns and consistency. We need to make sense of it all:
Youtube Video, Cognitive dissonance (Dissonant & Justified), Brad Wray, April 2011,4:31 minutes
But it turns out that really, as Kahneman demonstrates, we are not particularly good scientists after all. Yes, we have to grapple with the problems of interpreting evidence. Yes, we have to try and understand the world in order to reduce our own anxieties and make it a safer place. But, no, we do not do this particularly systematically or rationally. We are lazy and we are also as much artists as we are scientists. In fact, what we are is ‘story tellers’. We make up stories about how the world works – for ourselves and for others.
The significance for ROBOT ETHICS: The implications for A/ISs is that they must learn to see the world in a manner that is similar (or at least understandable) to the people around them. Also, they must have mechanisms to deal with ambiguous inputs and uncertain knowledge, because not much is straightforward when it comes to processing at the abstract level of ethics. Dealing with contradictory evidence by denial, forgetting and ignoring, as people often do, may not be the way we would like A/ISs to deal with ethical issues.
Sifting evidence is not the only way that we come to ‘know’. There is another method that, in many ways, is a lot more efficient and used just as often. This is to believe what somebody else says. So instead of having to understand and reconcile all the evidence yourself you can, as it were, delegate the responsibility to somebody you trust. This could be an expert, or a friend, or a God. After all, what does it matter whether what you (or anybody else) believe is true or not, so long as your needs are being met. If somebody (or something) repeatedly comes up with the goods, you learn to trust them and when you trust, you can breathe a sigh of relief – you no longer have to make the effort to evaluate the evidence yourself. The source of information is often just as important as the information itself. Despite the inconsistencies we believe the stories of those we trust, and if others trust us, they believe our stories.
Stories provide the explanations for what has happened and stories help us understand and predict what will happen. Our anxiety is most relieved by ‘a good story’. And while the story needs to have some resemblance to the evidence, and like in court can be challenged and cross-examined, what seems to matter most is that it is a ‘good’ story. And to be a ‘good’ story it must be interesting, revealing, surprising and challenging. Its consistency is just one factor. In fact, there can be many different stories, or accounts, of precisely the same incident or event – each account from a different perspective; interpreting, weighing and presenting the evidence from a different viewpoint or through a different value system. The ‘truth’ is not just how well the story accounts for the evidence but is also to do with a correspondence between the interpretive framework of the listener and that of the teller:
YouTube Video, The danger of a single story | Chimamanda Ngozi Adichie, TED, October 2009, 19:16 minutes
Both as individuals and as societies, we often deny, gloss over and suppress the inconsistencies. They can be conveniently forgotten or repressed long enough for something else to demand our attention and pre-occupy us. But also sometimes, for the sake of a ‘better’ story (often one that better reflects the biases in our own value system), the inconsistencies and the evidence about ourselves and the human condition fight back. Inconsistencies can re-emerge to create nagging doubts, and over time we start to wonder – is our story really true?
The significance for ROBOT ETHICS:Just like people, A/ISs will have to learn who to trust, identify and resolve inconsistencies in belief, and how to construct a variety of accounts of the world and their own decision making processes in order to explain themselves and generally communicate in forms that are understandable to people. Like in human dialogue, these accounts will need to bring out certain facets of it’s own beliefs, and afford certain interpretations, depending on the intent of the A/IS and taking into account a knowledge of the person or people it is in dialogue with. Unlike, in human dialogue, the intent of the A/IS must be to enhance the wellbeing of the people it serves (except when their intent is malicious with respect to other people), and to communicate transparently with this intent in mind.
Some Epistemological Assumptions
In these blog postings, I try not to take for granted any particular story about how we are and how we relate to each other? What really lies behind our motivations, decisions and choices? Is it the story that classical economists tell us about rational people in a world of perfect information? Is it the story neuroscientists tell us about how the brain works? Is it the story about the constant struggle between the id and the super-ego told to us by Freud? Is it the story that the advertising industry tell us about what we need for a more fulfilled life? Or is it the story that cognitive psychologists tell us about how we process information? Which account tells the best story? Can these different accounts be reconciled?
The epistemological view taken in this blog is eclectic, constructivist and pragmatic. 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.
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.
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.
The significance for ROBOT ETHICS:To embed ethical self-regulation in artificial Autonomous, Intelligent Systems (A/ISs) will require an understanding of how people learn, interpret, reflect and act on the world and may require a similar decision-making architecture. This is partly for the A/IS’s own ‘operating system’ but also so that it can model how the people around them operate so that it can engage with them ethically and effectively.
This Blog Post: ‘It’s Like This’ sets the epistemological framework for what follows in later posts. It’s the underlying assumptions about how we know, justify and explain what we know – both as individuals and in society.