Computational Perspectives: Fairness and Awareness in Data Analysis

Royal Statistical Society


London, UK

Neil D. Lawrence

Amazon and University of Sheffield


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


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

Benjamin Disraeli

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

Benjamin Disraeli 1804-1881

Mathematical Statistics

  • ‘Founded’ by Karl Pearson (1857-1936)

There are three types of lies: lies, damned lies and ‘big data’

Neil Lawrence 1972-?

‘Mathematical Data Science’

  • ‘Founded’ by ? (?-?)

Background: Big Data

  • Data is Pervasive phenomenon that affects all aspects of our activities

  • Data diffusiveness is both a challenge and an opportunity

Evolved Relationship

Evolved Relationship

Evolved Relationship

“Embodiment Factors”

compute ~10 gigaflops ~ 1000 teraflops?
communicate ~1 gigbit/s ~ 100 bit/s
10 ~ 1013

Evolved Relationship


  • This phenomenon has already revolutionised biology.

  • Large scale data acquisition and distribution.

  • Transcriptomics, genomics, epigenomics, ‘rich phenomics’.

Societal Effects

  • Automated decision making within the computer based only on the data.

  • A requirement to better understand our own subjective biases to ensure that the human to computer interface formulates the correct conclusions from the data.

Societal Effects

  • Shift in dynamic from the direct pathway between human and data to indirect pathway between human and data via the computer

  • This change of dynamics gives us the modern and emerging domain of data science


  1. Paradoxes of the Data Society

  2. Quantifying the Value of Data

  3. Privacy, loss of control, marginalization

Breadth vs Depth Paradox

  • Able to quantify to a greater and greater degree the actions of individuals

  • But less able to characterize society

  • As we measure more, we understand less


  • Perhaps greater preponderance of data is making society itself more complex

  • Therefore traditional approaches to measurement are failing

  • Curate’s egg of a society: it is only ‘measured in parts’

Wood or Tree

  • Can either see a wood or a tree.


  • Election polls (UK 2015 elections, EU referendum, US 2016 elections)

  • Clinical trial and personalized medicine

  • Social media memes

  • Filter bubbles and echo chambers

The Maths

\[ \mathbf{Y} = \begin{bmatrix} y_{1, 1} & y_{1, 2} &\dots & y_{1,p}\\ y_{2, 1} & y_{2, 2} &\dots & y_{2,p}\\ \vdots & \vdots &\dots & \vdots\\ y_{n, 1} & y_{n, 2} &\dots & y_{n,p} \end{bmatrix} \in \Re^{n\times p} \]

The Maths

\[ \mathbf{Y} = \begin{bmatrix} \mathbf{y}^\top_{1, :} \\ \mathbf{y}^\top_{2, :} \\ \vdots \\ \mathbf{y}^\top_{n, :} \end{bmatrix} \in \Re^{n\times p} \]

The Maths

\[ \mathbf{Y} = \begin{bmatrix} \mathbf{y}_{:, 1} & \mathbf{y}_{:, 2} & \dots & \mathbf{y}_{:, p} \end{bmatrix} \in \Re^{n\times p} \]

The Maths

\[p(\mathbf{Y}|\boldsymbol{\theta}) = \prod_{i=1}^n p(\mathbf{y}_{i, :}|\boldsymbol{\theta})\]

The Maths

\[p(\mathbf{Y}|\boldsymbol{\theta}) = \prod_{i=1}^n p(\mathbf{y}_{i, :}|\boldsymbol{\theta})\]

\[\log p(\mathbf{Y}|\boldsymbol{\theta}) = \sum_{i=1}^n \log p(\mathbf{y}_{i, :}|\boldsymbol{\theta})\]


  • Typically \(\boldsymbol{\theta} \in \Re^{\mathcal{O}(p)}\)

  • Consistency reliant on large sample approximation of KL divergence

\[ \text{KL}(P(\mathbf{Y})|| p(\mathbf{Y}|\boldsymbol{\theta}))\]

  • Minimization is equivalent to maximization of likelihood.

  • A foundation stone of classical statistics.

Large \(p\)

  • For large \(p\) the parameters are badly determined.

  • Large \(p\) small \(n\) problem.

  • Easily dealt with through definition.

The Maths

\[p(\mathbf{Y}|\boldsymbol{\theta}) = \prod_{j=1}^p p(\mathbf{y}_{:, j}|\boldsymbol{\theta})\]

\[\log p(\mathbf{Y}|\boldsymbol{\theta}) = \sum_{j=1}^p \log p(\mathbf{y}_{:, j}|\boldsymbol{\theta})\]

Breadth vs Depth

  • Modern Measurement deals with depth (many subjects) … or breadth lots of detail about subject.

  • But what about
    • \(p\approx n\)?
    • Stratification of populations: batch effects etc.

Does \(p\) Even Exist?

  • Massively missing data.

  • Classical bias towards tables.

  • Streaming data.

\[ \mathbf{Y} = \begin{bmatrix} y_{1, 1} & y_{1, 2} &\dots & y_{1,p}\\ y_{2, 1} & y_{2, 2} &\dots & y_{2,p}\\ \vdots & \vdots &\dots & \vdots\\ y_{n, 1} & y_{n, 2} &\dots & y_{n,p} \end{bmatrix} \in \Re^{n\times p} \]

General index on \(y\)


where \(\mathbf{x}\) might include time, spatial location …

Streaming data. Joint model of past, \(\mathbf{y}\) and future \(\mathbf{y}_*\)

\[p(\mathbf{y}, \mathbf{y}_*)\]

Prediction through:


Kolmogorov Consistency

  • From the sum rule of probability we have \[\begin{align*} p(\mathbf{y}|n^*) = \int p(\mathbf{y}, \mathbf{y}^*) \text{d}\mathbf{y}^* \end{align*}\]

    \(n^*\) is length of \(\mathbf{y}^*\).

  • Consistent if \(p(\mathbf{y}|n^*) = p(\mathbf{y})\)

  • Prediction then given by product rule \[\begin{align*} p(\mathbf{y}^*|\mathbf{y}) = \frac{p(\mathbf{y}, \mathbf{y}^*)}{p(\mathbf{y})} \end{align*}\]


Parametric Models

  • Kolmogorov consistency trivial in parametric model. \[\begin{align*} p(\mathbf{y}, \mathbf{y}^*) = \int \prod_{i=1}^n p(y_{i} | \boldsymbol{\theta})\prod_{i=1}^{n^*}p(y^*_i|\boldsymbol{\theta}) p(\boldsymbol{\theta}) \text{d}\boldsymbol{\theta} \end{align*}\]
  • Marginalizing \[\begin{align*} p(\mathbf{y}) = \int \prod_{i=1}^n p(y_{i} | \boldsymbol{\theta})\prod_{i=1}^{n^*} \int p(y^*_i|\boldsymbol{\theta}) \text{d}y^*_i p(\boldsymbol{\theta}) \text{d}\boldsymbol{\theta} \end{align*}\]

Parametric Bottleneck

  • Bayesian methods suggest a prior over \(\boldsymbol{\theta}\) and use posterior, \(p(\boldsymbol{\theta}|\mathbf{y})\) for making predictions. \[\begin{align*} p(\mathbf{y}^*|\mathbf{y}) = \int \prod_i p(y_i^* | \boldsymbol{\theta}) p(\boldsymbol{\theta}|\mathbf{y})\text{d}\boldsymbol{\theta} \end{align*}\]
  • Design time problem: parametric bottleneck. \[p(\boldsymbol{\theta} | \mathbf{y})\]

  • Streaming data could turn out to be more complex than we imagine.

Finite Storage

  • Despite our large interconnected brains, we only have finite storage.

  • Similar for digital computers. So we need to assume that we can only store a finite number of things about the data \(\mathbf{y}\).

  • This pushes us back towards parametric models.

Inducing Variables

  • Choose to go a different way.

  • Introduce a set of auxiliary variables, \(\mathbf{u}\), which are \(m\) in length.

  • They are like “artificial data”.

  • Used to induce a distribution: \(q(\mathbf{u}|\mathbf{y})\)

Making Parameters non-Parametric

  • Introduce variable set which is finite dimensional. \[ p(\mathbf{y}^*|\mathbf{y}) \approx \int p(\mathbf{y}^*|\mathbf{u}) q(\mathbf{u}|\mathbf{y}) \text{d}\mathbf{u} \]

  • But dimensionality of \(\mathbf{u}\) can be changed to improve approximation.

Variational Compression

  • Model for our data, \(\mathbf{y}\)


Variational Compression

  • Prior density over \(\mathbf{f}\). Likelihood relates data, \(\mathbf{y}\), to \(\mathbf{f}\).

\[p(\mathbf{y})=\int p(\mathbf{y}|\mathbf{f})p(\mathbf{f})\text{d}\mathbf{f}\]

Variational Compression

  • Prior density over \(\mathbf{f}\). Likelihood relates data, \(\mathbf{y}\), to \(\mathbf{f}\).
\[p(\mathbf{y})=\int p(\mathbf{y}|\mathbf{f})p(\mathbf{u}|\mathbf{f})p(\mathbf{f})\text{d}\mathbf{f}\text{d}\mathbf{u}\]

Variational Compression

\[p(\mathbf{y})=\int \int p(\mathbf{y}|\mathbf{f})p(\mathbf{f}|\mathbf{u})\text{d}\mathbf{f}p(\mathbf{u})\text{d}\mathbf{u}\]

Variational Compression

\[p(\mathbf{y})=\int \int p(\mathbf{y}|\mathbf{f})p(\mathbf{f}|\mathbf{u})\text{d}\mathbf{f}p(\mathbf{u})\text{d}\mathbf{u}\]

Variational Compression

\[p(\mathbf{y}|\mathbf{u})=\int p(\mathbf{y}|\mathbf{f})p(\mathbf{f}|\mathbf{u})\text{d}\mathbf{f}\]

Variational Compression


Variational Compression



  • Replace true \(p(\mathbf{u}|\mathbf{y})\) with approximation \(q(\mathbf{u}|\mathbf{y})\).

  • Minimize KL divergence between approximation and truth.

Also need

  • More classical statistics!
    • Like the ‘paperless office’
  • A better characterization of human (see later)

  • Larger studies (100,000 genome)
    • Combined with complex models: algorithmic challenges

Quantifying the Value of Data

There’s a sea of data, but most of it is undrinkable

We require data-desalination before it can be consumed!


  • How do we measure value in the data economy?
  • How do we encourage data workers: curation and management
  • Incentivization
  • Quantifying the value in their contribution

Credit Allocation

  • Direct work on data generates an enormous amount of ‘value’ in the data economy but this is unaccounted in the economy

  • Hard because data is difficult to ‘embody’

  • Value of shared data: Wellcome Trust 2010 Joint Statement (from the “Foggy Bottom” meeting)


  • Encourage greater interaction between application domains and data scientists

  • Encourage visualization of data

  • Adoption of ‘data readiness levels’

  • Implications for incentivization schemes

Privacy, Loss of Control and Marginalization

  • Society is becoming harder to monitor

  • Individual is becoming easier to monitor






Hate Speech or Political Dissent?

  • social media monitoring for ‘hate speech’ can be easily turned to political dissent monitoring


  • can become more sinister when the target of the marketing is well understood and the (digital) environment of the target is also so well controlled

Free Will

  • What does it mean if a computer can predict our individual behavior better than we ourselves can?


  • Potential for explicit and implicit discrimination on the basis of race, religion, sexuality, health status

  • All prohibited under European law, but can pass unawares, or be implicit


  • Credit scoring, insurance, medical treatment
  • What if certain sectors of society are under-represented in our aanalysis?
  • What if Silicon Valley develops everything for us?

Digital Revolution and Inequality?


  • Work to ensure individual retains control of their own data
  • We accept privacy in our real lives, need to accept it in our digital
  • Control of persona and ability to project

  • Need better technological solutions: trust and algorithms.


  • Need to increase awareness of the pitfalls among researchers
  • Need to ensure that technological solutions are being delivered not merely for few (#FirstWorldProblems)
  • Address a wider set of challenges that the greater part of the world’s population is facing


  • Data science offers a great deal of promise
  • There are challenges and pitfalls
  • It is incumbent on us to avoid them
  • Need new ways of thinking!
  • Mathematical Data Science

Many solutions rely on education and awareness