Post-Digital Transformation, Decision Making and Intellectual Debt

Introduction

The Gartner Hype Cycle

Cycle for ML Terms

Google Trends

❮ ❯

What is Machine Learning?

What is Machine Learning?

What is Machine Learning?

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

• To combine data with a model need:
• a prediction function \(f(\cdot)\) includes our beliefs about the regularities of the universe
• an objective function \(E(\cdot)\) defines the cost of misprediction.

Artificial Intelligence and Data Science

• AI aims to equip computers with human capabilities
  • Image understanding
  • Computer vision
  • Speech recognition
  • Natural language understanding
  • Machine translation

Supervised Learning for AI

• Dominant approach today:
  • Generate large labelled data set from humans.
  • Use supervised learning to emulate that data.
    • E.g. ImageNet Russakovsky et al. (2015)
• Significant advances due to deep learning
  • E.g. Alexa, Amazon Go

Data Science

• Arises from happenstance data.
• Differs from statistics in that the question comes after data collection.

Exercise: Score Yourself

• I am a data science:

1. follower (no visibility/influence)
2. some visibilty/influence
3. visibility and some influence
4. leader (lead on data and AI developments)

Embodiment and Intellectual Debt

Information and Embodiment

Claude Shannon

Embodiment Factors

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

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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

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

Neil Lawrence 1972-?

Mathematical Statistics

‘Mathematical Data Science’

Heider and Simmel (1944)

Evolved Relationship with Information

New Flow of Information

Evolved Relationship

Evolved Relationship

Intellectual Debt

GDPR Origins

How the GDPR May Help

How GDPR May Help

• Reflection on data eco-systems.
• GDPR: Good Data Practice Rules
• When viewed as best practice rather than regulation they hightlight problems in data ecosystems.

GDPR in Practice

• Understand the lawful basis
• For websites: provide a “Privacy Notice”

\(p\)-Fairness and \(n\)-Fairness

Reflexive and Reflective Intelligence

\[\text{reflect} \Longleftrightarrow \text{reflex}\]

The Big Data Paradox

• We collect more data, but we understand less.

Wood or Tree

Big Model Paradox

• Add complexity to the model to make it realistic.
• Move model “beyond human intuition”
• But model still falls well short of mark in terms of representing reality

Complexity in Action

Data Selective Attention Bias

BMI Steps Data

BMI Steps Data Analysis

A Hypothesis as a Liability

“ ‘When someone seeks,’ said Siddhartha, ‘then it easily happens that his eyes see only the thing that he seeks, and he is able to find nothing, to take in nothing. […] Seeking means: having a goal. But finding means: being free, being open, having no goal.’ ”

Hermann Hesse

The Scientific Process

Number Theatre

Data Theatre

The Art of Statistics

Increasing Need for Human Judgment

The domain of human judgment is increasing.

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

Case Study

Case Study: Face Masks

DELVE Overview

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

Data at the Heart

• Use data to answer policy questions.
• Make international comparisons for input.
• Challenges: around getting data.

Breakout

• As pre-read material, you were provided with a report summary and links to the full report and responses collated by the Science Media Centre.
  • In your groups, discuss the summary and these responses.
• For both the comments and the responses put yourself in the position of a government minister.
  • What would you do and why?
  • Which comments and recommendations were helpful and which weren’t?

Links

• Report: https://rs-delve.github.io/reports/2020/05/04/face-masks-for-the-general-public.html
• Responses: https://www.sciencemediacentre.org/expert-reaction-to-review-of-evidence-on-face-masks-and-face-coverings-by-the-royal-society-delve-initiative/
• Decision making with uncertainty (Diggle et al., 2020)

Bringing it Back

Dealing with Intellectual Debt

What we Did at Amazon

Are Right a Lot

Leaders are right a lot. They have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.

Dive Deep

Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdote differ. No task is beneath them.

Thoughtsday

• Dealing with highly operational decision making.

What we did in DELVE

Data as a Convener

• Data allows externalisation of cognition.
• Even when not existing, can ask: What data would we want?

Breakout: What would you do in your company?

Conclusion