On Ethics and Algorithms

franck-v-g29arbbvPjo-unsplash
Photo by Franck V. on Unsplash

An article on the front page of the Observer, Revealed: how drugs giants can access your health records, caught my eye this week. In summary the article highlights that the Department of Health and Social Care (DHSC) has been selling the medical data of NHS patients to international drugs companies and have “misled” the public that the information contained in the records would be “anonymous”.

The data in question is collated from GP surgeries and hospitals and, according to “senior NHS figures”, can “routinely be linked back to individual patients’ medical records via their GP surgeries.” Apparently there is “clear evidence” that companies have identified individuals whose medical histories are of “particular interest.” The DHSC have replied by saying it only sells information after “thorough measures” have been taken to ensure patient anonymity.

As with many articles like this it is frustrating when some of the more technical aspects are not fully explained. Whilst I understand the importance of keeping their general readership on board and not frightening them too much with the intricacies of statistics or cryptography it would be nice to know a bit more about how these records are being made anonymous.

There is a hint of this in the Observer report when it states that the CPRD (the Clinical Practice Research Datalink ) says the data made available for research was “anonymous” but, following the Observer’s story, it changed the wording to say that the data from GPs and hospitals had been “anonymised”. This is a crucial difference. One of the more common methods of ‘anonymisation’  is to obscure or redact some bits of information. So, for example, a record could have patient names removed and ages and postcodes “coarsened”, that is only the first part of a postcode (e.g. SW1A rather than SW1A 2AA)  are included and ages are placed in a range rather than using someones actual age (e.g. 60-70 rather than 63).

The problem with anonymising data records is that they are prone to what is referred to as data re-identification or de-anonymisation. This is the practice of matching anonymous data with publicly available information in order to discover the individual to which the data belongs. One of the more famous examples of this is the competition that Netflix organised encouraging people to improve its recommendation system by offering a $50,000 prize for a 1% improvement. The Netflix Prize was started in 2006 but abandoned in 2010 in response to a lawsuit and Federal Trade Commission privacy concerns. Although the dataset released by Netflix to allow competition entrants to test their algorithms had supposedly been anonymised (i.e. by replacing user names with a meaningless ID and not including any gender or zip code information) a PhD student from the University of Texas was able to find out the real names of people in the supplied dataset by cross-referencing the Netflix dataset with Internet Movie Database (IMDB) ratings which people post publicly using their real names.

Herein lies the problem with the anonymisation of datasets. As Michael Kearns and Aaron Roth highlight in their recent book The Ethical Algorithm, when an organisation releases anonymised data they can try and make an intelligent guess as to which bits of the dataset to anonymise but it can be difficult (probably impossible) to anticipate what other data sources either already exist or could be made available in the future which could be used to correlate records. This is the reason that the computer scientist Cynthia Dwork has said “anonymised data isn’t” – meaning either it isn’t really anonymous or so much of the dataset has had to be removed that it is no longer data (at least in any useful way).

So what to do? Is it actually possible to release anonymised datasets out into the wild with any degree of confidence that they can never be de-anonymised? Thankfully something called differential privacy, invented by the aforementioned Cynthia Dwork and colleagues, allows us to do just that. Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in that dataset.

To understand how differential privacy works consider this example*. Suppose we want to conduct a poll of all people in London to find out who have driven after taking non-prescription drugs. One way of doing this is to randomly sample a suitable number of Londoners, asking them if they have ever driven whilst under the influence of drugs. The data collected could be entered into a spreadsheet and various statistics, e.g. number of men, number of women, maybe ages etc derived. The problem is that whilst collecting this information lots of compromising personal details may be collected which, if the data were stolen, could be used against them.

In order to avoid this problem consider the following alternative. Instead of asking people the question directly, first ask them to flip a coin but not to tell us how it landed. If the coin comes up heads they tell us (honestly) if they have driven under the influence. If it comes up tails however they tell us a random answer then flip the coin again and tell us “yes” if it comes up heads or “no” if it is tails. This polling protocol is a simple randomised algorithm which is a form of differential privacy. So how does this work?

differential privacy
If your answer is no, the randomised response answers no two out of three times. It answers no only one out of three times if your answer is yes. Diagram courtesy Michael Kearns and Aaron Roth, The Ethical Algorithm 2020

When we ask people if they have driven under the influence using this protocol half the time (i.e. when the coin lands heads up) the protocol tells them to tell the truth. If the protocol tells them to respond with a random answer (i.e. when the coin lands tails up), then half of that time they just happen to randomly tell us the right answer. So they tell us the right answer 1/2 + ((1/2) x (1/2)) or three-quarters of the time. The remaining one quarter of the time they tell us a lie. There is no way of telling true answers from lies. Surely though, this injection of randomisation completely masks the true results and the data is now highly error prone? Actually, it turns out, this is not the case.

Because we know how this randomisation is introduced we can reverse engineer the answers we get to remove the errors and get an approximation of the right answer. Here’s how. Suppose one-third of people in London have actually driven under the influence of drugs. So of the one-third who have truthfully answered “yes” to the question, three-quarters of those will answer “yes” using the protocol, that is 1/3 x 3/4 = 1/4. Of the two-thirds who have a truthful answer of “no”, one-quarter of those will report “yes”, that is 2/3 x 1/4 = 1/6. So we expect 1/4 + 1/6 = 5/12 ~ 1/3 of the population to answer “yes”.

So what is the point of doing the survey like this? Simply put it allows the true answer to be hidden behind the protocol. If the data were leaked and an individual from it was identified as being suspected of driving under the influence then they could always argue they were told to say “yes” because of the way the coins fell.

In the real world a number of companies including the US census, Apple, Google and Privitar Lens use differential privacy to limit the disclosure of private information about individuals whose information is in public databases.

It would be nice to think that the NHS data that is supposedly being used by US drug companies was protected by some form of differential privacy. If it were, and if this could be explained to the public in a reasonable and rational way, then surely we would all benefit both in the knowledge that our data is safe and is maybe even being put to good use in protecting and improving our health. After all, wasn’t this meant to be the true benefit of living in a connected society where information is shared for the betterment of all our lives?

*Based on an example from Kearns and Roth in The Ethical Algorithm.

Cummings needs data scientists, economists and physicists (oh, and weirdos)

Dominic Cummings
Dominic Cummings – Image Copyright Business Insider Australia

To answer my (rhetorical) question in this post I think it’s been pretty much confirmed since the election that Dominic Cummings is, in equal measures, the most influential, disruptive, powerful and dangerous man in British politics right now. He has certainly set the cat amongst the pigeons in this blog post where he has effectively by-passed the civil service recruitment process by advertising for people to join his ever growing team of SPAD’s (special advisors). Cummings is looking for data scientists, project managers, policy experts and assorted weirdos to join his team. (Interestingly today we hear that the self-proclaimed psychic Uri Geller has applied for the job believing he qualifies because of the super-talented weirdo aspect of the job spec.)

Cummings is famed for his wide reaching reading tastes and the job spec also cites a number of scientific papers potential applicants “will be considering”. The papers mentioned are broadly in the areas of complex systems and the use of maths and statistics in forecasting which give an inkling into the kind of problems Cummings sees as those that need to be ‘fixed’ in the civil service as well as the government at large (including the assertion that “Brexit requires many large changes in policy and in the structure of decision-making”).

Like many of his posts, this particular one tends to ramble and also be contradictory. In one paragraph he’s saying that you “do not need a PhD” but then in the very next one saying you  “must have exceptional academic qualifications from one of the world’s best universities with a PhD or MSc in maths or physics.”

Cummings also returns to one of his favourite topics which is that of the failure of projects – mega projects in particular – and presumably those that governments tend to initiate and not complete on time or to budget (or at all). He’s an admirer of some of the huge project successes of yesteryear such as The Manhattan Project (1940s), ICBMs (1950s) and Apollo (1960s) but reckons that since then the Pentagon has “systematically de-programmed itself from more effective approaches to less effective approaches from the mid-1960s, in the name of ‘efficiency’.” Certainly the UK government is no stranger to some spectacular project failures itself both in the past and present (HS2 and Crossrail being two more contemporary examples of not so much failures but certainly massive cost overruns).

However as John Naughton points out here  “these inspirational projects have some interesting things in common: no ‘politics’, no bureaucratic processes and no legal niceties. Which is exactly how Cummings likes things to be.” Let’s face it both Crossrail and HS2 would be a doddle of only you could do away with all those pesky planning proposals and environmental impact assessments you have to do and just move people out of the way quickly – sort of how they do things in China maybe?

Cummings believes that now is the time to bring together the right set of people with a sufficient amount of cognitive diversity and work in Downing Street with him and other SPADs to start to address some of the wicked problems of government. One ‘lucky’ person will be his personal assistant, a role which he says will “involve a mix of very interesting work and lots of uninteresting trivia that makes my life easier which you won’t enjoy.” He goes on to say that in this role you “will not have weekday date nights, you will sacrifice many weekends — frankly it will hard having a boy/girlfriend at all. It will be exhausting but interesting and if you cut it you will be involved in things at the age of ~21 that most people never see.” That’s quite some sales pitch for a job!

What this so called job posting is really about though is another of Cummings abiding obsessions (which he often discusses in his blog) that the government in general, and civil service in particular (which he groups together as “SW1”), is basically not fit for purpose because it is scientifically and technologically illiterate as well as being staffed largely with Oxbridge humanities graduates. The posting is also a thinly veiled attempt at pushing the now somewhat outdated ‘move fast and break things” mantra of Silicon Valley. An approach that does not always play out well in government (Universal Credit anyone). I well remember my time working at the DWP (yes, as a consultant) where one of the civil servants with whom I was working said that the only problem with disruption in government IT was that it was likely to lead to riots on the streets if benefit payments were not paid on time. Sadly, Universal Credit has shown us that it’s not so much street riots that are caused but a demonstrable increase in demand for food banks. On average, 12 months after roll-out, food banks see a 52% increase in demand, compared to 13% in areas with Universal Credit for 3 months or less.

Cummings of course would say that the problem is not so much that disruption per se causes problems but rather the ineffective, stupid and incapable civil servants who plan and deploy such projects are at fault, hence the need for hiring the right ‘assorted weirdos’ who will bring new insights that fusty old civil servants cannot see. Whilst he may well be right that SW1 is lacking in deep technical experts as well as great project managers and ‘unusual’ economists he needs to realise that government transformation cannot succeed unless it is built on a sound strategy and good underlying architecture. Ideas are just thoughts floating in space until they can be transformed into actions that result in change which takes into account that the ‘products’ that governments deal with are people not software and hardware widgets.

This problem is far better articulated by Hannah Fry when she says that although maths has, and will continue to have, the capability to transform the world those who apply equations to human behaviour fall into two groups: “those who think numbers and data ultimately hold the answer to everything, and those who have the humility to realise they don’t.”

Possibly the last words should be left to Barack Obama who cautioned Silicon Valley’s leaders thus:

“The final thing I’ll say is that government will never run the way Silicon Valley runs because, by definition, democracy is messy. This is a big, diverse country with a lot of interests and a lot of disparate points of view. And part of government’s job, by the way, is dealing with problems that nobody else wants to deal with.

So sometimes I talk to CEOs, they come in and they start telling me about leadership, and here’s how we do things. And I say, well, if all I was doing was making a widget or producing an app, and I didn’t have to worry about whether poor people could afford the widget, or I didn’t have to worry about whether the app had some unintended consequences — setting aside my Syria and Yemen portfolio — then I think those suggestions are terrific. That’s not, by the way, to say that there aren’t huge efficiencies and improvements that have to be made.

But the reason I say this is sometimes we get, I think, in the scientific community, the tech community, the entrepreneurial community, the sense of we just have to blow up the system, or create this parallel society and culture because government is inherently wrecked. No, it’s not inherently wrecked; it’s just government has to care for, for example, veterans who come home. That’s not on your balance sheet, that’s on our collective balance sheet, because we have a sacred duty to take care of those veterans. And that’s hard and it’s messy, and we’re building up legacy systems that we can’t just blow up.”

Now I think that’s a man who shows true humility, something our current leaders (and their SPADs) could do with a little more of I think.

 

The story so far…

 

Photo by Joshua Sortino on Unsplash
Photo by Joshua Sortino on Unsplash

It’s hard to believe that this year is the 30th anniversary of Tim Berners-Lee’s great invention, the World-Wide Web, and that much of the technology that enabled his creation is still less than 60 years old. Here’s a brief history of the Internet and the Web, and how we got to where we are today, in ten significant events.

 

#1: 1963 – Ted Nelson begins developing a model for creating and using linked content he calls hypertext and hypermedia. Hypertext is born.

#2: 1969 – The first message is sent over the ARPANET from computer science Professor Leonard Kleinrock’s laboratory at University of California, Los Angeles to the second network node at Stanford Research Institute. The Internet is born.

#3: 1969 – Charles Goldfarb, leading a small team at IBM, developed the first markup language, called Generalized Markup Language, or GML. Markup languages are born.

#4: 1989 – Tim Berners-Lee whilst working at CERN publishes his paper, Information Management: A Proposal. The World Wide Web (WWW) is born.

#5: 1993Mosaic, a graphical browser aiming to bring multimedia content to non-technical users (images and text on the same page) is invented by Marc Andreessen. The web browser is born.

#6: 1995 – Jeff Bezos launches Amazon “earth’s biggest bookstore” from a garage in Seattle. E-commerce is born.

#7: 1998 – The Google company is officially launched by Larry Page and Sergey Brin to market Google Search. Web search is born.

#8: 2003Facebook (then called FaceMash but changed to The Facebook a year later) is founded by Mark Zuckerberg with his college roommate and fellow Harvard University student Eduardo Saverin. Social media is born.

#9: 2007 – Steve Jobs launches the iPhone at MacWorld Expo in San Francisco. Mobile computing is born.

#10: 2018 – Tim Berners-Lee instigates act II of the web when he announces a new initiative called Solid, to reclaim the Web from corporations and return it to its democratic roots. The web is reborn?

I know there have been countless events that have enabled the development of our modern Information Age and you will no doubt think others should be included in preference to some of my suggestions. Also, I suspect that many people will not have heard of my last choice (unless you are a fairly hardcore computer type). The reason I have added this one is because I think/hope it will start to address what is becoming one of the existential threats of our age, namely how we survive in a world awash with data (our data) that is being mined and used without us knowing, much less understanding, the impact of such usage. Rather than living in an open society in which ideas and data are freely exchanged and used to everyones benefit we instead find ourselves in an age of surveillance capitalism which, according to this source, is defined as being:

…the manifestation of George Orwell’s prophesied Memory Hole combined with the constant surveillance, storage and analysis of our thoughts and actions, with such minute precision, and artificial intelligence algorithmic analysis, that our future thoughts and actions can be predicted, and manipulated, for the concentration of power and wealth of the very few.

In her book The Age of Surveillance Capitalism, Shoshana Zuboff provides a sweeping (and worrying) overview and history of the techniques that the large tech companies are using to spy on us in ways that even George Orwell would have found alarming. Not least because we have voluntarily given up all of this data about ourselves in exchange for what are sometimes the flimsiest of benefits. As Zuboff says:

Thanks to surveillance capitalism the resources for effective life that we seek in the digital realm now come encumbered with a new breed of menace. Under this new regime, the precise moment at which our needs are met is also the precise moment at which our lives are plundered for behavioural data, and all for the sake others gain.

Tim Berners-Lee invented the World-Wide Web then gave it away so that all might benefit. Sadly some have benefited more than others, not just financially but also by knowing more about us than most of us would ever want or wish. I hope for all our sakes the work that Berners-Lee and his small group of supporters is doing make enough progress to reverse the worst excesses of surveillance capitalism before it is too late.

What Are Digital Skills?

Photo by Sabri Tuzcu on Unsplash
Photo by Sabri Tuzcu on Unsplash

There have been many, many reports both globally and within the UK bemoaning the lack of digital skills in todays workforce. The term digital skills is somewhat amorphous however and can mean different things to different people.

To more technical types it can mean the ability to write code, develop new computer hardware or have deep insights into how networks are set up and configured. To less digital savvy people it may just mean the ability to operate digital technology such as tablets and mobile phones or how to find information on the world wide web or even just fill out forms on web sites (e.g. to apply for a bank account).

A recent report from the CBI, Delivering Skills for the New Economy , which comes up with a number of concrete steps on how the UK might address a shortage of digital skills, suggests the following as a way of categorising these skills. Useful if we are to find way in which to address their scarcity.

  • Basic digital skills: Businesses define basic digital skills in similar terms. For most businesses this means computer literacy such as familiarity with Microsoft Office; handling digital information and content; core skills such as communication and problem-solving; and understanding how digital technologies work. This understanding of digital technologies includes understanding how data can be used to glean new insights, how social media provides value for a business or how an algorithm or piece of digitally-enabled machinery works.
    Basic digital skills: Businesses define basic digital skills in similar terms. For most businesses this means computer literacy such as familiarity with Microsoft Office; handling digital information and content; core skills such as communication
    and problem-solving; and understanding how digital technologies work. This understanding of digital technologies includes understanding how data can be used to glean new insights, how social media provides value for a business or how an algorithm or piece of digitally-enabled machinery works.
  • Advanced digital skills: Businesses also broadly agree on the definitions of advanced digital skills. For most businesses, these include software engineering and development (77%), data analytics (77%), IT support and system maintenance (81%) and digital marketing and sales (72%). Businesses have highlighted their increasing need for specific advanced digital skills, including programming, visualisation, machine learning, data analytics, app development, 3D printing expertise, cloud awareness and cybersecurity.
    It is important that a good grounding in the basic (core) skills is given to as many people as possible. The so called digital natives or “Gen Zs” (at least in first world countries) have grown up knowing nothing else but the world wide web, touch screen technology and pervasive social media. Older generations, less so. All need this information if they are to operate effectively in the “New Economy” (or know enough to actively disengage from it if they choose to do so).

The basic skills will also allow for a more critical assessment of what advanced digital skills should be considered if making choices about jobs or if people just need to understand what social media companies they should or should not be using or how artificial intelligence might affect their career prospects.

I would argue that a basic level of advanced digital knowledge is also a requirement so that everyone can play a more active role in this modern economy and understand the implications of technology.

Do Startups Need Enterprise Architectures?

And by implication, do they need Enterprise Architects?

For the last few years I have been meeting with startup and scaleup founders, in and around my local community of Birmingham in the United Kingdom, offering thoughts and advice on how they should be thinking about the software and systems architectures of the applications and platforms they are looking to build. Inevitably, as the discussions proceed and we discuss not just what they need now (the minimum viable architecture if you like) but what they might need in the future, as they hopefully scale and grow, the question arises of how and why they need to worry about architecture now and can’t leave it until later when hopefully they’ll have a bit more investment and be able to hire people to do it “properly”.

To my mind this is a bit of a chicken and egg type question. Do they set the groundwork properly now, in the hope that this will then form the foundation on which they grow or, do they just hack something together “quick and dirty” to get paying customers onto the platform and only then think about architecture and go through the pain of moving onto a more robust platform that will be future proof? Or do they try and have their cake and eat it and somehow do a bit of both? Spending too much time on pointless architecture may mean you never get to launch because you run out of time or money. On the other hand if you don’t have at least the basics of an architecture you might launch but quickly collapse if your system cannot support an unexpected surge in users or maybe a security breach.

These are important questions to consider and ones which need to be addressed early on in the startup cycle, at least to the degree that if an enterprise architecture (EA) is not laid out, the reasons for not doing so are clear. In other words has an architecture decision been made for having an EA or not?

No startup being formed today should consider that IT and business are separate endeavours. Whether you are a new cake shop selling patisseries using locally sourced ingredients or a company launching a new online social media enterprise that hopes to take onFacebook, IT will be fundamental to your business model. Decisions you make when starting out could live with you for a long, long time (and remember just five years is a long time in the IT world).

Interestingly it’s often the business folk that understand the need for doing architecture early on rather than IT people. Possibly this is because business people are looking at costs not just today but over a five year period and want to minimise the amount of IT rework they need to do. IT folk just see it as bringing in new cool toys to play with every year or so (and may not even be around in five years time anyway as they are more likely to be contract staff).

Clearly the amount of EA a startup or scaleup needs has to be in proportion to the size and ambitions of the business. If they are a one or two man band who just needs a minimum viable product to show to investors then maybe a simple solution architecture captured using, for example, the C4 model for software architecture will suffice.

For businesses who are beyond their first seed rounding of funding and are into series A or B investment then I do believe they should be thinking seriously about using part of that investment to build some elements of an EA. Whether you use TOGAF or Zachman or any other framework doesn’t really matter. What does matter is that you capture the fundamental organisation of the system you wish to build to help offset the amount of technical debt you are prepared to take on.

Here are some pointers and guidelines for justifying an EA to your founders, investors and employees.

Reducing Your Technical Debt

Technical debt is what you get when you release not-quite-right code out into the world. Every minute spent on not-quite-right code counts as interest on that debt. The more you can do to ensure “right-first-time” deployments the lower your maintenance costs and the more of your budget you’ll have to spend on innovation rather than maintenance. The lower, in other words, are your technical debt interest repayments. For a scaleup this is crucial. As much of every dollar of your investment funding that can go on marketing or innovating your platform leads to an improved bottom line, more customers and overall making you attractive to potential buyers. This leads to…

Enhancing Your Exit Strategy

Many, if not most, startups have an exit strategy which usually involves being brought out by a larger company making the founders and initial investors rich beyond their wildest dreams enabling them to go on and found other startups (or retire onto a yacht in the Caribbean). Those large companies are going to want to see what they are getting for their money and having a well thought through and documented EA is a step on the way to proving that. The large company does not want to become another HP who got its fingers badly burnt in the infamous Autonomy buyout.

To Infinity and Beyond

Maybe your founder is another Mark Zuckerberg who refuses the advances of other companies and instead decides to go it alone in conquering the world. If successful there is going to be some point at which your user base grows beyond what you ever thought possible. If that’s the case hopefully you architected your system to be extensible as well as manageable to support all those users. Whilst there are no guarantees at least having the semblance of an EA will reduce the chances of having to go back to square one and rebuilding your whole technology base from scratch.

Technology Changes

Hardly an Earth shattering statement but often one which you tend not to consider when starting out. As technologists, we all know the ever increasing pace of change of the technology we deal with and the almost impossible task of keeping up with such change. Whilst there may be no easy ways to deal with the complete left-field, unexpected (who’d have thought blockchain would be a thing just 10 years go) having a well thought through business, data, application and technology architecture which contains loosely coupled components with well understood functionality and relationships at least means you can change or upgrade those components when the technology improves.

K.I.S.S (Keep It Simple Stupid)

It is almost certain that the first version of the system you come up with will be more complicated than it needs to be (which is different from saying the system is complex). Anyone who invests in V1.0 or even V2.0 of a system is almost certainly buying more complexity than they need. That complexity will manifest itself in how easy (or not) it is to maintain or upgrade the solution or how often it fails or under-performs. By following at least part of a well thought through architecture development method (ADM) which you iterate over a few times in the early stages of the development process you should be able to spot complexity, redundant components or ones which should not be built by you at all but which should be procured instead as commercial of the shelf (COTS) products. Bear in mind of course that COTS products are not always what they seem and using EA and an ADM to evaluate such products can save much grief further down the road.

It’s All About Strategy

For a startup, or even a scaleup, getting your strategy right is key. Investors often want to see a three or even five year plan which shows how you will execute on your strategy. Without having a strategy which maps out where you want to go how will you ever be able to build a plan that shows how you will get there?

Henry Mintzberg developed the so called 5P’s of Strategy as being:

  1. Strategy as Planning – large planning exercises, defining the future of the organisation.
  2. Strategy as a Ploy – to act to influence a competitor or market.
  3. Strategy as a Position – to act to take a chosen place in the chosen market.
  4. Strategy as Pattern – the strategy that has evolved over time.
  5. Strategy as a Perspective – basing the strategy on cultural values or similar non-tangible concepts.

For startups, where it is important to understand early on your ploy (who are you going to disrupt), your position (where do you want to place yourself and what differentiates you in the market) and perspective (what are your cultural values that makes you stand out), an EA can help you formulate these. Your business architecture can be developed to show how it supports these P’s and your technology architecture can show how it implements them.

As an aside one of the most effective tools I have used for understanding strategy and mapping and deciding what should be built versus what should be bought are Wardley maps. Not usually part of an EA or an ADM but worthwhile investigating.

So, in summary, whilst having an EA for a startup will in no way guarantee its success I believe not having an EA could inhibit its smooth growth and in the long run cost more in rework and refactoring (i.e. to reduce the amount of technical debt) than the cost of putting together, at least an outline EA, in the first place. How much EA is right for your startup/scaleup is up to you but it’s worthwhile giving some thought to this as part of your initial planning.

 

 

Is Dominic Cummings the Most Influential* Person in British Politics?

Dominic Cummings. Photograph from parliament.tv

If you want to understand the likely trajectory of the new Conservative government you could do worse than study the blog posts of Dominic Cummings. In case you missed this announcement amongst all the cabinet reshuffling that happened last week, Cummings is to be Boris Johnson’s new “special adviser”.

*For what it’s worth I could equally have used any of the adjectives ‘disruptive’, ‘powerful’ or ‘dangerous’ here I think.

Cummings has had three previous significant advisory roles either in UK government or in support of political campaigns:

  • Campaign director at Business for Sterling (the campaign against the UK joining the Euro) between 1999 and 2002;
  • Special adviser to Michael Gove at the Deprtment for Education between 2010 and 2014;
  • Campaign Director Vote Leave between 2015 and 2016.

Much has already been written about Cummings, some of it more speculative and wishful thinking than factual I suspect, that you can find elsewhere (David Cameron was alleged to have called Cummings a “career psychopath“). What is far more interesting to me is what Cummings writes in his sometimes rambling blog posts, and what I focus on here.

In his capacity advising Gove at the DfE Cummings wrote a 240-page essay, Some thoughts on education and political priorities which was about transforming Britain into a “meritocratic technopolis”. Significantly during Gove’s tenure as education minister we saw far more emphasis on maths and grammar being taught from primary age (8-11) and teaching of ‘proper’ computer science in secondary schools (i.e. programming rather than how to use Microsoft Office products). Clearly his thoughts were being acted upon.

Given that his advise has been implemented before it does not seem unreasonable that a study of Cummings blog posts may give us some insight into what ideas we may see enacted by the current government. Here are a few of Cummings most significant thoughts from my reading of his blog. I have only included thoughts on his more recent posts, mainly those from his time in exile between the end of the Vote Leave campaign and now. Many of these build on previous posts anyway but more significantly are most relevant to what we are about to see happen in Johnsons new government. The name of the post is highlighted in italics and also contains a hyperlink to the actual post.

High performance government, ‘cognitive technologies’, Michael Nielsen, Bret Victor, & ‘Seeing Rooms’

Cummings is very critical of the UK civil service, as well as government ministers, that he maintains do not make decisions based on facts and hard data but more often on intuition, feelings and inevitably their own biases and prejudices. In this post he suggests that ‘systems’ should be implemented to help run government. These would be things like:

  • Cognitive toolkits and AI that would support rational decision-making and help to decide what is possible as well as what is not (and why).
  • Prediction tournaments that could easily and cheaply be extended to consider ‘clusters’ of issues around themes like Brexit to improve policy and project management.
  • Red Teams and pre-mortems to help combat groupthink and “normal cognitive biases” . He advocates that Red Teams should work ‘above’ the Cabinet Office to ensure diversity of opinions, fight groupthink and other standard biases that make sure lessons are learned and government blunders avoided or at least minimised.
  • Seeing rooms that would replace the antiquated meeting spaces found in much of government (e.g. the Cabinet room) and use state of the art screens, IT and conference facilities to ensure better and more accurate decision making.

Two people mentioned often in this post by Cummings are Bret Victor and Michael Nielsen. Victor is a an interface designer, computer scientist, and electrical engineer who writes and talks on the future of technology. Nielsen is also a writer and computer scientist with an interest in neural networks and deep learning. The way Cummings immerses himself in fields outside of his area of expertise (he studied Ancient & Modern History at Oxford) and makes connections between different disciplines is itself instructive. Often the best ideas come from having such a cross-disciplinary approach to life without confining oneself to your particular comfort zone.

‘Systems’ thinking — ideas from the Apollo space programme on effective management and a possible ‘systems politics’

This post, published as a paper in February 2017, looks at what Cummings refers to as ‘mission critical’ political institutions” i.e. government departments with huge budgets, complex programs of work like HS2 (or Brexit) and those dealing with emergency situations such as terrorist incidents and wars. It looks at how disasters can (or could) be avoided by deploying “high performance man-machine teams” where the individuals involved are selected on the basis of their training and education as well “incentives”. The paper considers the development of new ideas about managing complex projects that were used by George Mueller to put men on the moon in 1969.

This quote sums up Cummings concerns with our current political institutions:

The project of rewiring institutions and national priorities is a ‘systems’ problem requiring a systems solution. Could we develop a systems politics that applies the unrecognised simplicities of effective action? The tale of George Mueller will be useful for all those thinking about how to improve government performance dramatically, reliably, and quantifiably.

The paper gives a potted history of systems engineering ideas and practices bringing in everyone from the military strategist John Boyd to the mathematician John von Neumann and along the way. Cummings is also fond of comparing the success of NASA’s mission to put a man on the moon and bring him safely home to the failure of the European Launcher Development Organisation (ELDO) to even launch a rocket. The difference being (according to Cummings) that NASA’s success was due to “a managerial effort, no less prodigious than the technological one”.

Cummings core lessons for politics which he believes “could be applied to re-engineering political institutions such as Downing Street” are many and varied. but here are a few, which even after less than a week of Boris Johnsons government I think we are seeing being enacted. How that is happening are my italics in the below.

  • Organisation-wide orientation. Everybody in a large organisation must understand as much about the goals and plans as possible. The UK is leaving the EU on 31st October 2019.
  • There must be an overall approach in which the most important elements fit together, including in policy, management, and communications. Johnson has completely gutted May’s cabinet and everyone new onboard has allegedly been told they must be on message, tow the party line and vote with the government in any upcoming parliamentary votes.
  • You need a complex mix of centralisation and decentralisation.While overall vision, goals, and strategy usually comes from the top, it is vital that extreme decentralisation dominates operationally so that decisions are fast and unbureaucratic. Interesting that Johnsons first act as prime minister is to visit the regions (not Brussels) promising them various amounts of money presumably to do just this.
  • People and ideas are more important than technology. Computers and other technologies can help but Colonel Boyd’s dictum holds: people, ideas, technology — in that order. It is too early to see if this approach will be implemented. Certainly government does not have a good track record when it comes to implementing IT systems so it will be interesting to see if the ‘solution’ to the Irish backstop does end up being IT driven.

‘Expertise’, prediction and noise, from the NHS killing people to Brexit

What this post is about is probably best summed up by Cummings own words near the beginning of the article:

In SW1 (i.e. Whitehall) now, those at the apex of power practically never think in a serious way about the reasons for the endemic dysfunctional decision-making that constitutes most of their daily experience or how to change it. What looks like omnishambles to the public and high performers in technology or business is seen by Insiders, always implicitly and often explicitly, as ‘normal performance’. ‘Crises’ such as the collapse of Carillion or our farcical multi-decade multi-billion ‘aircraft carrier’ project occasionally provoke a few days of headlines but it’s very rare anything important changes in the underlying structures and there is no real reflection on system failure.

Although this post covers some of the same ground as previous ones it shows how Cummings ideas on how to tackle the key problems of government are beginning to coalesce, probably best summed up in the following:

One of the most powerful simplicities in all conflict (almost always unrecognised) is: ‘winning without fighting is the highest form of war’. If we approach the problem of government performance at the right level of generality then we have a chance to solve specific problems ‘without fighting’ — or, rather, without fighting nearly so much and the fighting will be more fruitful.

If you see the major problem of government as solving the wicked problem of Brexit it will be interesting to see how, and if, Cummings manages to tackle this particular issue. After all it has already led to two prime ministers resigning or being pushed out and even Boris Johnsons’ tenure is not guaranteed if he fails to deliver Brexit or calls an election that gains a greatly increased majority that allows him to push his ideas through.

The Digital Activist’s View

Few would argue that a government that based its decisions on data, more scientific methods and industry best practices around project and systems management would not be a good thing. However, using data to understand people and their needs is very different to using data to try and influence what people think, how they vote and the way they go about their daily lives. Something that Vote Leave (and by implication Cummings) have been accused of by proliferating fake new stories during the leave campaign. In short who is going to sit above the teams that position themselves above our decision makers?

One of Cummings pet hates is the whole Whitehall/civil service infrastructure. He sees it as being archaic and not fit for purpose and an organisation whose leaders come from a particular educational background and set of institutions that religiously follow the rules as well as outdated work practices no matter what. To quote Cummings from this paper:

The reason why Gove’s team got much more done than ANY insider thought was possible – including Cameron and the Perm Sec – was because we bent or broke the rules and focused very hard on a) replacing rubbish officials and bringing in people from outside and b) project management.

The danger here is that by bringing in some of the changes Cummings is advocating just risks replacing one set of biases/backgrounds with another. After all the industries that are spawning both the tools and techniques he is advocating (i.e. predominantly US West Coast tech companies) are hardly known for their gender/ethnic diversity or socially inclusive policies. They too tend to follow particular practices, some of which may work when running a startup business but less so when running a country. I remember being told myself when discussing ‘disruption’ with a civil servant in one of the UK’s large departments of state that the problem with disruption in government is that it can lead to rioting in the streets if it goes wrong.

There is also a concern that by focusing on the large, headline grabbing government departments (e.g. Cabinet Office, DWP, MoD etc) you miss some of the good work being done by lesser departments and agencies within them. I’m thinking of Ordnance Survey and HM Land Registry in particular (both currently part of the Department for Business, Energy and Industrial Strategy and which I have direct experience of working with). The Ordnance Survey (which is classified as a ‘public corporation) has successfully mapped the UK for over 100 years and runs a thriving commercial business for its maps and mapping services. Similarly HM Land Registry has kept several trillion pounds worth of the nations land and property assets safe in digital storage for around 50 years and is looking at innovative ways of extending its services using technologies such as blockchain.

Sometimes when one’s entire working life is spent in the bubble that is Westminster it is easy to miss the innovative thinking that is going on outside. Often this is most successful when that thinking is being done by practitioners. For a good example of this see the work being done by the consultant neurologist Dr. Mark Wardle including this paper on using algorithms in healthcare.

If UK government really is as devoid of skills as Cummings is implying there is the danger they will try to ‘import’ skills by employing ever larger armies of consultants. This approach is fraught with danger as there is no guarantee the consultants will be as well read and immersed in the issues as Cummings hopes. The consultants will of course tell a good story but in my experience (i.e. as a consultant, not in government) unless they are well managed their performance is unlikely to be better than the people they are trying to replace. Cummings acknowledges this potential issue when he asks how we “distinguish between fields dominated by real expertise and those dominated by confident ‘experts’ who make bad predictions?

Finally, do we really want Whitehall to become a department of USA Inc by climbing into bed with a country which, under the presidency of Trump, seems to be leaning ever more rightward? As part of any post-Brexit trade deal it is likely the US will be seeking a greater say in running not just our civil service but health service, schools and universities. All at a time when its tech companies seem to be playing an ever more intrusive part in our daily lives.

So what is the answer to the question that is the title of this post? As someone who trained as a scientist and has worked in software architecture and development all of my life I recognise how some of the practices Cummings advocates could, if implemented properly, lead to change for the better in UK government at this critical time in the nations history. However we need to realise that ultimately by following the ideas of one, or a small group of people, we run the risk of replacing one dogma with another. Dogma always has to be something we are prepared to rip up no matter where or who it comes from. Sometimes we have to depend on what the military strategist John Boyd (one of Cummings influences) calls “intuitive competence” in order to deal with the novelty that permeates human life.

I also think that a government run by technocrats will not necessarily lead to a better world. Something I think even Cummings hints at when he says:

A very interesting comment that I have heard from some of the most important scientists involved in the creation of advanced technologies is that ‘artists see things first’ — that is, artists glimpse possibilities before most technologists and long before most businessmen and politicians.

At the time of writing Boris Johnsons’ government is barely one week old. All we are seeing for now are the headline grabbing statements and sound bites. Behind the scenes though we can be sure that Cummings and his team of advisers are doing much string pulling and arm bending of ministers and civil servants alike. We shall soon see not just what the outcomes of this are, but how long Boris Johnson survives.

How could blockchain drive a more responsible approach to engaging with the arts?

Image Courtesy of Tran Mai Khanh
Image Courtesy of Tran Mai Khanh

This is the transcript of a talk I gave at the 2018 Colloquium on Responsibility in Arts, Heritage, Non-profit and Social Marketing at the University of Birmingham Business School  on 17th September 2018.

Good morning everyone. My name is Peter Cripps and I work as a Software Architect for IBM in its Blockchain Division.

As a Software Architect my role is to help IBM’s clients understand blockchain and to architect systems built on this exciting new technology.

My normal world is that of finance, commerce and government computer systems that we all interact with on a day to day basis. In this talk however I’d like to discuss something a little bit different from my day to day role. I would like to explore with you how blockchain could be used to build a trust based system for the arts world that I believe could lead to a more responsible way for both creators and consumers of art to interact and transact to the mutual benefit of all parties.

First however let’s return to the role of the Software Architect and explain how two significant architectures have got us to where we are today (showing that the humble Software Architect really can change the world).

Architects take existing components and…

Seth on Architects
Seth on Architects

This is one of my favourite definitions of what architects do. Although Seth was talking about architects in the construction industry, it’s a definition that very aptly applies to Software Architects as well. By way of illustration here are two famous examples of how architects took some existing components and assembled them in very interesting ways.

1989: Tim Berners-Lee invents the World Wide Web

Tim Berners Lee and the World Wide Web
Tim Berners Lee and the World Wide Web

The genius of Tim Berners-Lee, when he invented the World Wide Web in 1989, was that he brought together three arcane technologies (hypertext, mark-up languages and Internet communication protocols) in a way no one had thought of before and literally transformed the world by democratising information. Recently however, as Berners Lee discusses in an interview in Vanity Fair, the web has begun to reveal its dark underside with issues of trust, privacy and so called fake news dominating much of the headlines over the past two years.

Interestingly, another invention some 20 years later, promises to address some of the problems now being faced by a society that is increasingly dependent on the technology of the web.

2008: Satoshi Nakamoto invents Bitcoin

Satoshi Nakamoto and Bitcoin
Satoshi Nakamoto and Bitcoin

Satoshi Nakamoto’s paper Bitcoin: A Peer-to-Peer Electronic Cash System, that introduced the world to Bitcoin in 2009, also used three existing ideas (distributed databases, cryptography and proof-of-work) to show how a peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. His genius idea, in a generalised form, was a platform that creates a new basis of trust for business transactions that could ultimately lead to a simplification and acceleration of the economy. We call this blockchain. Could blockchain, the technology that underpins Bitcoin, be the next great enabling technology that not only changes the world (again) but also puts back some of the trust in a the World Wide Web?

Blockchain: Snake oil or a miracle cure?

Miracle Cure or Snake Oil?
Miracle Cure or Snake Oil?

Depending on your point of view, and personal agenda, blockchain either promises to be a game changing technology that will help address such issues such as the world’s refugee crisis and the management of health supply chains or is the most over-hyped, terrifying and foolish technology ever. Like any new technology we need to be very careful to separate the hype from the reality.

What is blockchain?

Setting aside the hype, blockchain, at its core, is all about trust. It provides a mechanism that allows parties over the internet, who not only don’t trust each other but may not even know each other, to exchange ‘assets’ in a secure and traceable way. These assets can be anything from physical items like cars and diamonds or intangible ones such as music or financial instruments.

Here’s a definition of what a blockchain is.

An append-only, distributed system of record (a ledger) shared across a business network that provides transaction visibility to all involved participants.

Let’s break this down:

  1. A blockchain is ‘append only’. That means once you’ve added a record (a block) to it you cannot remove it.
  2. A blockchain is ‘distributed’ which means the ledger, or record book, is not just sitting in one computer or data centre but is typically spread around several.
  3. A ‘system of record’ means that, at its heart, a blockchain is a record book describing the state of some asset. For example that a car with a given VIN is owned by me.
  4. A blockchain is ‘shared’ which means all participants get their own copy, kept up to date and synchronised with all other copies.
  5. Because it’s shared all participants have ‘visibility’ of the records or transactions of everyone else (if you want them to).

A business blockchain network has four characteristics…

Business blockchains can be characterised as having these properties:

Consensus

All parties in the network have to agree that a transaction is valid and that it can be added as a new block on the ledger. Gaining such agreement is referred to as consensus and various ways of reaching such consensus are available. One such consensus technique, which is used by the Bitcoin blockchain is referred to as proof-of-work. In Bitcoin proof-of-work is referred to as mining which is a highly compute intensive process as miners must compete to solve a mathematically complex problem to earn new coins. Because of its complexity, mining uses large amounts of computing power. In 2015 it was estimated that the Bitcoin mining network consumed about the same amount of energy as the whole of Ireland!

Happily, however, not all blockchain networks suffer from this problem as they do all not use proof-of-work as a consensus mechanism. Hyperledger, an open source software project owned and operated by the Linux Foundation , provides several different technologies that do not require proof-of-work as a consensus mechanism and so have vastly reduced energy requirements. Hyperledger was formed by over 20 founding companies in December 2015. Hyperledger blockchains are finding favour in the worlds of commerce, government and even the arts! Further, because Hyperledger is an open source project, anyone with access to the right skillset can build and operate their own blockchain network.

Immutability

This means that once you add a new block onto the ledger, it cannot be removed. It’s there for ever and a day. If you do need to change something then you must add a new record saying what that change is. The state of an asset on a blockchain is then the sum of all blocks that refer to asset.

Provenance

Because you can never remove a block from the ledger you can always trace back in time the history of assets being described on the ledger and therefore determine, for example, where it originated or how ownership has changed over time.

Finality

The shared ledger is the place that all participants agree stores ‘the truth’. Because the ledgers records cannot be removed and everyone has agreed them being recorded on there, that is the final source of truth.

… with smart contracts controlling who does what

Another facet of blockchain is the so called ‘smart contract’. Smart contracts are pieces of code that autonomously run on the blockchain, in response to some event, without the interference of a human being. Smart contracts change the state of the blockchain and are responsible for adding new blocks to the chain. In a smart contract the computer code is law and, provided all parties have agreed in advance the validity of that code, once it has run changes to the blockchain cannot be undone but become immutable. The blockchain therefore acts as a source of permanent knowledge about the state of an asset and allows the provenance of any asset to be understood. This is a fundamental difference between a blockchain and an ordinary database. Once a record is entered it cannot be removed.

Some blockchain examples

Finally, for this quick tour of blockchain, let’s take a look at a couple of industry examples that IBM has been working on with its clients.

The first is a new company called Everledger which aims to record on a blockchain the provenance of high value assets, such as diamonds. This allows people to know where assets have come from and how ownership has changed over time avoiding fraud and issues around so called ‘blood diamonds’ which can be used to finance terrorism and other illegal activities.

The second example is the IBM Food Trust Network, a consortium made up of food manufacturers, processors, distributors, retailers and others that allow for food to be tracked from ‘farm to fork’. This allows, for example, the origin of a particular food to be quickly determined in the case of a contamination or outbreak of disease and for only effected items to be taken out the supply chain.

What issues can blockchain address in the arts world?

In the book Artists Re:Thinking the Blockchain various of the contributors discuss how blockchain could be used to create new funding models for the arts by the “renegotiation of the economic and social value of art” as well as helping artists “to engage with new kinds of audiences, patrons and participants.” (For another view of blockchain and the arts see the documentary The Blockchain and Us).

I also believe blockchain could help tackle some of the current problems around trust and lack of privacy on the web as well as address issues around the accumulation of large amounts of user generated content at virtually no cost to the owners in what the American computer scientist Jaron Lanier calls “siren-servers” .

Let’s consider two aspects of the art world that blockchain could address:

Trust

As a creator how do I know people are using my art work legitimately? As a consumer how do I know the creator of the art work is who they say they are and the art work is authentic?

Value

As a creator how do I get the best price for my art work? As a consumer how do I know I am not paying too much for an art work?

Challenges/issues of the global art market (and how blockchain could address them)

Let’s drill down a bit more into what some of these issues are and how a blockchain network could begin to address them. Here’s a list of nine key issues that various players in the world of arts say impacts the multi-billion pound art market and which blockchain could help address in the ways I suggest.

Art Issues
To be clear, not all of these issues will be addressed by technology alone. As with any system that is introduced it needs not only the buy-in of the existing players but also a sometimes radical change in the underlying business model that the current system has developed. ArtChain is one such company that is looking to use blockchain to address some of these issues. Another is the online photography community YouPic.

Introducing YouPic Blockchain

YouPic is an online community for photographers which allows photographers to not only share their images but also receive payment. YouPic is in the process of implementing a blockchain that allows photographers to retain more control over their images. For example:

  1. Copyright attribution.
  2. Image tracking and copyright tools.
  3. Smart contracts for licensing

Every image has a unique fingerprint so when you look up the fingerprint or a version of the image it points out all of the licensing information the creator has listed.

The platform could, for example, search the web to identify illicit uses of images and if identified contact the creator to notify them of a potential copyright breach.

You could also use smart contracts to manage you images automatically, e.g. receive payments in different currencies, or maybe you want to distribute payment to other contributors or just file a claim if your image is used without your consent.

ArtLedger

ArtLedger

ArtLedger is a sandbox I am developing for exploring some of these ideas. It’s open source and available on GitHub. I have a very rudimentary proof of concept running in the IBM Cloud that allows you to interact with a blockchain network with some of these actors.

I’m encouraging people to go onto my GitHub project, take a look at the code and the instructions for getting it working and have a play with the live system. I will be adapting it over time to add more functions and see how the issues in the previous stage could be addressed as well as exploring funding models for how such a network could become established and grow.

Summary

So, to summarise:

  • Blockchains can provide a system that engenders trust through the combined attributes of: Consensus; Immutability; Provenance; Finality.
  • Consortiums of engaged participants should build networks where all benefit.
  • Many such networks are at the early stages of development. It is still early days for the technology but results are promising and, for the right use cases, systems based on blockchain have the promise of another step change in driving the economy in a fairer and more just way.
  • For the arts world blockchain holds the promise of engaging with new kinds of audiences, patrons and participants and maybe even the creation of new funding models.