Abstract

With the world watching case numbers increase and publics and policymakers scrutinising projections from epidemiological models, the covid-19 pandemic brought with it increased attention on the use of data to inform policy. Alongside this scrutiny came a new wave of interest in the ability of data and artificial intelligence (AI) to help tackle major scientific and social challenges: could our increasing ability to collect, combine and interrogate large datasets lead to new insights that unlock more effective policy responses?

Lies and Damned Lies

There are three types of lies: lies, damned lies and statistics

Benjamin Disraeli 1804-1881

Benjamin Disraeli said¹ that there three types of lies: lies, damned lies and statistics. Disraeli died in 1881, 30 years before the first academic department of applied statistics was founded at UCL. If Disraeli were alive today, it is likely that he’d rephrase his quote:

There are three types of lies, lies damned lies and big data.

Why? Because the challenges of understanding and interpreting big data today are similar to those that Disraeli faced in governing an empire through statistics in the latter part of the 19th century.

The quote lies, damned lies and statistics was credited to Benjamin Disraeli by Mark Twain in his autobiography. It characterizes the idea that statistic can be made to prove anything. But Disraeli died in 1881 and Mark Twain died in 1910. The important breakthrough in overcoming our tendency to overinterpet data came with the formalization of the field through the development of mathematical statistics.

Data has an elusive quality, it promises so much but can deliver little, it can mislead and misrepresent. To harness it, it must be tamed. In Disraeli’s time during the second half of the 19th century, numbers and data were being accumulated, the social sciences were being developed. There was a large scale collection of data for the purposes of government.

The modern ‘big data era’ is on the verge of delivering the same sense of frustration that Disraeli experienced, the early promise of big data as a panacea is evolving to demands for delivery. For me, personally, peak-hype coincided with an email I received inviting collaboration on a project to deploy “Big Data and Internet of Things in an Industry 4.0 environment.” Further questioning revealed that the actual project was optimization of the efficiency of a manufacturing production line, a far more tangible and realizable goal.

The antidote to this verbage is found in increasing awareness. When dealing with data the first trap to avoid is the games of buzzword bingo that we are wont to play. The first goal is to quantify what challenges can be addressed and what techniques are required. Behind the hype fundamentals are changing. The phenomenon is about the increasing access we have to data. The manner in which customers information is recorded and processes are codified and digitized with little overhead. Internet of things is about the increasing number of cheap sensors that can be easily interconnected through our modern network structures. But businesses are about making money, and these phenomena need to be recast in those terms before their value can be realized.

Mathematical Statistics

Karl Pearson (1857-1936), Ronald Fisher (1890-1962) and others considered the question of what conclusions can truly be drawn from data. Their mathematical studies act as a restraint on our tendency to over-interpret and see patterns where there are none. They introduced concepts such as randomized control trials that form a mainstay of the our decision making today, from government, to clinicians to large scale A/B testing that determines the nature of the web interfaces we interact with on social media and shopping.

Figure: Karl Pearson (1857-1936), one of the founders of Mathematical Statistics.

Their movement did the most to put statistics to rights, to eradicate the ‘damned lies.’ It was known as ‘mathematical statistics’. Today I believe we should look to the emerging field of data science to provide the same role. Data science is an amalgam of statistics, data mining, computer systems, databases, computation, machine learning and artificial intelligence. Spread across these fields are the tools we need to realize data’s potential. For many businesses this might be thought of as the challenge of ‘converting bits into atoms.’ Bits: the data stored on computer, atoms: the physical manifestation of what we do; the transfer of goods, the delivery of service. From fungible to tangible. When solving a challenge through data there are a series of obstacles that need to be addressed.

Firstly, data awareness: what data you have and where its stored. Sometimes this includes changing your conception of what data is and how it can be obtained. From automated production lines to apps on employee smart phones. Often data is locked away: manual log books, confidential data, personal data. For increasing awareness an internal audit can help. The website data.gov.uk hosts data made available by the UK government. To create this website the government’s departments went through an audit of what data they each hold and what data they could make available. Similarly, within private buisnesses this type of audit could be useful for understanding their internal digital landscape: after all the key to any successful campaign is a good map.

Secondly, availability. How well are the data sources interconnected? How well curated are they? The curse of Disraeli was associated with unreliable data and unreliable statistics. The misrepresentations this leads to are worse than the absence of data as they give a false sense of confidence to decision making. Understanding how to avoid these pitfalls involves an improved sense of data and its value, one that needs to permeate the organization.

The final challenge is analysis, the accumulation of the necessary expertise to digest what the data tells us. Data requires intepretation, and interpretation requires experience. Analysis is providing a bottleneck due to a skill shortage, a skill shortage made more acute by the fact that, ideally, analysis should be carried out by individuals not only skilled in data science but also equipped with the domain knowledge to understand the implications in a given application, and to see opportunities for improvements in efficiency.

‘Mathematical Data Science’

As a term ‘big data’ promises much and delivers little, to get true value from data, it needs to be curated and evaluated. The three stages of awareness, availability and analysis provide a broad framework through which organizations should be assessing the potential in the data they hold. Hand waving about big data solutions will not do, it will only lead to self-deception. The castles we build on our data landscapes must be based on firm foundations, process and scientific analysis. If we do things right, those are the foundations that will be provided by the new field of data science.

Today the statement “There are three types of lies: lies, damned lies and ‘big data’” may be more apt. We are revisiting many of the mistakes made in interpreting data from the 19th century. Big data is laid down by happenstance, rather than actively collected with a particular question in mind. That means it needs to be treated with care when conclusions are being drawn. For data science to succede it needs the same form of rigour that Pearson and Fisher brought to statistics, a “mathematical data science” is needed.

You can also check my blog post on Lies, Damned Lies and Big Data.

New Flow of Information

Figure: The trinity of human, data and computer, and highlights the modern phenomenon. The communication channel between computer and data now has an extremely high bandwidth. The channel between human and computer and the channel between data and human is narrow. New direction of information flow, information is reaching us mediated by the computer.

Embodiment Factors

Table: The embodiment factor is the ratio of compute to communicate in the model.

compute	\[\approx 100 \text{ gigaflops}\]	\[\approx 16 \text{ petaflops}\]
communicate	\[1 \text{ gigbit/s}\]	\[100 \text{ bit/s}\]
(compute/communicate)	\[10^{4}\]	\[10^{14}\]

See “Living Together: Mind and Machine Intelligence” Lawrence (2017)

There is a fundamental limit placed on our intelligence based on our ability to communicate. Claude Shannon founded the field of information theory. The clever part of this theory is it allows us to separate our measurement of information from what the information pertains to².

Shannon measured information in bits. One bit of information is the amount of information I pass to you when I give you the result of a coin toss. Shannon was also interested in the amount of information in the English language. He estimated that on average a word in the English language contains 12 bits of information.

Given typical speaking rates, that gives us an estimate of our ability to communicate of around 100 bits per second (Reed and Durlach, 1998). Computers on the other hand can communicate much more rapidly. Current wired network speeds are around a billion bits per second, ten million times faster.

When it comes to compute though, our best estimates indicate our computers are slower. A typical modern computer can process make around 100 billion floating point operations per second, each floating point operation involves a 64 bit number. So the computer is processing around 6,400 billion bits per second.

It’s difficult to get similar estimates for humans, but by some estimates the amount of compute we would require to simulate a human brain is equivalent to that in the UK’s fastest computer (Ananthanarayanan et al., 2009), the MET office machine in Exeter, which in 2018 ranks as the 11th fastest computer in the world. That machine simulates the world’s weather each morning, and then simulates the world’s climate in the afternoon. It is a 16 petaflop machine, processing around 1,000 trillion bits per second.

Bandwidth Constrained Conversations

Figure: Conversation relies on internal models of other individuals.

Figure: Misunderstanding of context and who we are talking to leads to arguments.

Embodiment factors imply that, in our communication between humans, what is not said is, perhaps, more important than what is said. To communicate with each other we need to have a model of who each of us are.

To aid this, in society, we are required to perform roles. Whether as a parent, a teacher, an employee or a boss. Each of these roles requires that we conform to certain standards of behaviour to facilitate communication between ourselves.

Control of self is vitally important to these communications.

The high availability of data available to humans undermines human-to-human communication channels by providing new routes to undermining our control of self.

The consequences between this mismatch of power and delivery are to be seen all around us. Because, just as driving an F1 car with bicycle wheels would be a fine art, so is the process of communication between humans.

If I have a thought and I wish to communicate it, I first of all need to have a model of what you think. I should think before I speak. When I speak, you may react. You have a model of who I am and what I was trying to say, and why I chose to say what I said. Now we begin this dance, where we are each trying to better understand each other and what we are saying. When it works, it is beautiful, but when misdeployed, just like a badly driven F1 car, there is a horrible crash, an argument.

DELVE Overview

The DELVE Initiative was convened by the Royal Society early in the pandemic in response for a perceived need to increase provide policy advice for the UK’s response to covide, with an initial focus on exit strategy from the first lock down.

Figure: The Royal Society announces the DELVE group to tackle the COVID-19 crisis. https://royalsociety.org/news/2020/04/royal-society-convenes-data-analytics-group-to-tackle-COVID-19/.

DELVE will contribute data driven analysis to complement the evidence base informing the UK’s strategic response, by:

• Analysing national and international data to determine the effect of different measures and strategies on a range of public health, social and economic outcomes
• Using emerging sources of data as new evidence from the unfolding pandemic comes to light
• Ensuring that the work of this group is coordinated with others and communicated as necessary both nationally and internationally

Delve Timeline

• First contact 3rd April
• First meeting 7th April
• First working group 16th April

The Delve initiative is a group that was convened by the Royal Society to help provide data-driven insights about the pandemic, with an initial focus on exiting the first lockdown and particular interest in using the variation of strategies across different international governments to inform policy.

Right from the start, data was at the heart of what DELVE does, but the reality is that little can be done without domain expertise and often the data we required wasn’t available.

However, even when it is not present, the notion of what data might be needed can also have a convening effect, bringing together multiple disciplines around the policy questons at hand. The Delve Data Readiness report (The DELVE Initiative, 2020a) makes recommendations for how we can improve our processes around data, but this talk also focuses on how data brings different disciplines together around data.

Any policy question can be framed in a number of different ways - what are the health outcomes; what is the impact on NHS capacity; how are different groups affected; what is the economic impact – and each has different types of evidence associated with it. Complex and uncertain challenges require efforts to draw insights together from across disciplines.

Sustained engagement between government and academia plays an important role in building mutual understanding about what each can deliver. Core to DELVE’s work was the intention that research questions be framed in ways that would resonate with the policy challenges being seen in government.

What is Machine Learning?

\[ \text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

The Big Data Paradox

The big data paradox is the modern phenomenon of “as we collect more data, we understand less.” It is emerging in several domains, political polling, characterization of patients for trials data, monitoring twitter for political sentiment.

I like to think of the phenomenon as relating to the notion of “can’t see the wood for the trees.” Classical statistics, with randomized controlled trials, improved society’s understanding of data. It improved our ability to monitor the forest, to consider population health, voting patterns etc. It is critically dependent on active approaches to data collection that deal with confounders. This data collection can be very expensive.

In business today, it is still the gold standard, A/B tests are used to understand the effect of an intervention on revenue or customer capture or supply chain costs.

Figure: New beech leaves growing in the Gribskov Forest in the northern part of Sealand, Denmark. Photo from wikimedia commons by Malene Thyssen, http://commons.wikimedia.org/wiki/User:Malene.

The new phenomenon is happenstance data. Data that is not actively collected with a question in mind. As a result, it can mislead us. For example, if we assume the politics of active users of twitter is reflective of the wider population’s politics, then we may be misled.

However, this happenstance data often allows us to characterise a particular individual to a high degree of accuracy. Classical statistics was all about the forest, but big data can often become about the individual tree. As a result we are misled about the situation.

The phenomenon is more dangerous, because our perception is that we are characterizing the wider scenario with ever increasing accuracy. Whereas we are just becoming distracted by detail that may or may not be pertinent to the wider situation.

This is related to our limited bandwidth as humans, and the ease with which we are distracted by detail. The data-inattention-cognitive-bias.

Big Model Paradox

The big data paradox has a sister: the big model paradox. As we build more and more complex models, we start believing that we have a high-fidelity representation of reality. But the complexity of reality is way beyond our feeble imaginings. So we end up with a highly complex model, but one that falls well short in terms of reflecting reality. The complexity of the model means that it moves beyond our understanding.

Computer Conversations

Figure: Conversation relies on internal models of other individuals.

Figure: Misunderstanding of context and who we are talking to leads to arguments.

Similarly, we find it difficult to comprehend how computers are making decisions. Because they do so with more data than we can possibly imagine.

In many respects, this is not a problem, it’s a good thing. Computers and us are good at different things. But when we interact with a computer, when it acts in a different way to us, we need to remember why.

Just as the first step to getting along with other humans is understanding other humans, so it needs to be with getting along with our computers.

Embodiment factors explain why, at the same time, computers are so impressive in simulating our weather, but so poor at predicting our moods. Our complexity is greater than that of our weather, and each of us is tuned to read and respond to one another.

Their intelligence is different. It is based on very large quantities of data that we cannot absorb. Our computers don’t have a complex internal model of who we are. They don’t understand the human condition. They are not tuned to respond to us as we are to each other.

Embodiment factors encapsulate a profound thing about the nature of humans. Our locked in intelligence means that we are striving to communicate, so we put a lot of thought into what we’re communicating with. And if we’re communicating with something complex, we naturally anthropomorphize them.

We give our dogs, our cats and our cars human motivations. We do the same with our computers. We anthropomorphize them. We assume that they have the same objectives as us and the same constraints. They don’t.

This means, that when we worry about artificial intelligence, we worry about the wrong things. We fear computers that behave like more powerful versions of ourselves that will struggle to outcompete us.

In reality, the challenge is that our computers cannot be human enough. They cannot understand us with the depth we understand one another. They drop below our cognitive radar and operate outside our mental models.

The real danger is that computers don’t anthropomorphize. They’ll make decisions in isolation from us without our supervision, because they can’t communicate truly and deeply with us.

Increasing Need for Human Judgment

Figure: Diane Coyle’s Fitzwilliam Lecture where she emphasises as data increases, human judgment is more needed.

The domain of human judgment is increasing.

How these firms use knowledge. How do they generate ideas?

Data as a Convener

To improve communication, we need to ‘externalise cognition’: have objects that are outside our brains, are persistent in the real world, that we can combine with our individual knowledge. Doing otherwise leaves us imagining the world as our personal domain-utopias, ignoring the ugly realities of the way things actual progress.

Data can provide an excellent convener, because even if it doesn’t exist it allows conversations to occur about what data should or could exist and how it might allow us to address the questions of importance.

Models, while also of great potential value in externalising cognition, can be two complex to have conversations about and they can entrench beliefs, triggering model induced blindness (a variation on Kahneman’s theory induced blindness (Kahneman, 2011)).

Figure: Models can also be used to externalise cognition, but if the model is highly complex it’s difficult for two individuals to understand each others’ models. This shuts down conversation, often “mathematical intimidation” is used to shut down a line of questioning. This is highly destructive of the necessary cognitive diversity.

Bandwidth constraints on individuals mean that they tend to focus on their own specialism. This can be particularly problematic for those on the more theoretical side, because mathematical models are complex, and require a lot of deep thought. However, when communicating with others, unless they have the same in depth experience of mathematical modelling as the theoreticians, the models do not bring about good information coherehnce. Indeed, many computational models themselves are so complex now that no individual can understand the model whole.

Figure: Data can be queried, but the simplest query, what data do we need? Doesn’t even require the data to exist. It seems data can be highly effective for convening a multidisciplinary conversation.

Fritz Heider referred to happenings that are “psychologically represented in each of the participants” (Heider, 1958) as a preqequisite for conversation. Data is a route to that psychological representation.

Note: my introduction to Fritz Heider was through a talk by Nick Chater in 2010, you can read Nick’s thoughts on these issues in his book, The Mind is Flat (Chater, 2019).

Delve

Delve Reports

1. Facemasks 4th May 2020 (The DELVE Initiative, 2020b)
2. Test, Trace, Isolate 27th May 2020 (The DELVE Initiative, 2020c)
3. Nosocomial Infections 6th July 2020 (The DELVE Initiative, 2020d)
4. Schools 24th July 2020 (The DELVE Initiative, 2020e)
5. Economics 14th August 2020 (The DELVE Initiative, 2020f)
6. Vaccines 1st October 2020 (The DELVE Initiative, 2020g)
7. Data 24th November 2020 (The DELVE Initiative, 2020a)

There is lots of hope for the role data science and AI could play, but we’re still a way off from being AI-ready. Further attention is needed on some of the foundational issues around data use – access, skills, culture – before we can begin to talk in earnest about deploying AI. [link here to data readiness]

Delve Data Report

The DELVE Initiative was established with the ambition that data science could play a role in helping develop policy responses to the COVID-19 pandemic, by identifying lessons from the responses of other countries or by combining datasets to generate novel insights. Such analysis requires access to data, which could come from both official statistics, or from so-called happenstance data, generated as a by-product of daily activities. Drawing from a multidisciplinary team of domain experts in policy, public health, economics, education, immunology, epidemiology, and social science, alongside statisticians, mathematicians, computer scientists and machine learning scientists, DELVE set out to provide advice and analysis that could feed into live policy decisions.

Our report focusses on what more we can do to ensure that this data is readily available (The DELVE Initiative, 2020a).

Delve Data Report: Recommendations

• Government should update the statutory objective of the Office for National Statistics (ONS) to accommodate trustworthy access to happenstance data to generate national and local statistics. Such statistics are required on very short time frames to facilitate fast decision-making for the nation in the rapidly evolving circumstances of a national emergency.

• The ONS should collaborate closely with the Information Commissioner’s Office (ICO) to formulate a standardized qualification for data access, equivalent to a ‘data driving license’ that would demonstrate trustworthiness and ensure that qualified experts can get rapid access to different data types with the appropriate standardized ethical and legal training in place.

• Government should fund interdisciplinary pathfinder data projects. These projects should require collaborations between industries, run across government departments and integrate different academic expertise. Each project should target a specific policy question. Beyond the pathfinder role, the projects will leave a legacy in the form of expertise and guidance in understanding the stages of the data-sharing pipeline. Priority areas for pathfinder projects include:

  • Nowcasting of economic metrics: At least one of these pathfinder projects should create a close collaboration between Cabinet Office and Treasury around nowcasting of classical economic metrics (such as GDP) from happenstance data (e.g. payments data). Efficient resourcing and strategic implementation of data sharing projects will only be possible if Treasury and Cabinet Office are aligned on plausible benefits and costs of data sharing projects.

  • Mobility data: Another project should drive a step-change in the use of mobility data for public policy. To achieve this, the ONS should act as the trusted body to convert happenstance data into high-frequency population mobility statistics. One pathfinder project should produce daily views of population mobility between geographic regions, aggregated from origin to destination counts from mobile phone operators.

Delivering a rapid response requires the ability to quickly convene teams from across disciplines (and often institutions) around a key question. To facilitate this, we also used ideas from blog post on open data science. to facilitate communication and understanding.

Conclusions

The particular circumstances of the Covid-19 pandemic have highlighted the challenges of integrating scientific ideas to answer policy questions. In this talk, we’ve given a formal introduction to the problem, the difficulty of communicating between individuals (particularly from different domains) and reviewed the ideas and solutions we used in the Delve initiative.

Recommendations from the DELVE Data report suggest that more effort needs to be placed into working in this manner in normal circumstances, so that when an emergency occurs we are better prepared to deal with the questions we face.

When we combine these difficult challenges with complex models, we need to put more effort into decomposing our models so that they may be calibrated and re-integrated at appropriate fidelities.

Thanks!

For more information on these subjects and more you might want to check the following resources.

• twitter: @lawrennd
• podcast: The Talking Machines
• newspaper: Guardian Profile Page
• blog: http://inverseprobability.com

References