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– It’s All Too Creepy

As concern about privacy and use of personal data grows, solutions are starting to emerge.

This week I attended an excellent symposium on ‘The Digital Person’ at Wolfson College Cambridge, organised by HATLAB.

The HATLAB consortium have developed a platform where users can store their personal data securely. They can then license others to use selected parts of it (e.g. for website registration, identity verification or social media) on terms that they, the user, is in control of.

The Digital Person
The Digital Person
This turns the table on organisations like Facebook and Google who have given users little choice about the rights over their own data, or how it might be used or passed on to third parties. GDPR is changing this through regulation. HATLAB promises to change it through giving users full legal rights to their data – an approach that very much aligns with the trend towards decentralisation and the empowerment of individuals. The HATLAB consortium, led by Irene Ng, is doing a brilliant job in teasing out the various issues and finding ways of putting the user back in control of their own data.

Highlights

Every talk at this symposium was interesting and informative. Some highlights include:


  • Misinformation and Business Models: Professor Jon Crowcroft
  • Taking back control of Personal Data: Professor Max van Kleek
  • Ethics-Theatre in Machine Learning: Professor John Naughton
  • Stop being creepy: Getting Personalisation and Recommendation right: Irene Ng

There was also some excellent discussion amongst the delegates who were well informed about the issues.

See the Slides

Fortunately I don’t have to go into great detail about these talks because thanks to the good organisation of the event the speakers slide sets are all available at:

https://www.hat-lab.org/wolfsonhat-symposium-2019

I would highly recommend taking a look at them and supporting the HATLAB project in any way you can.


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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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