Challenges in Data Science

ENBIS Meeting

University of Sheffield, UK

Neil D. Lawrence

Amazon and University of Sheffield

@lawrennd inverseprobability.com

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

“Embodiment Factors”

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

Evolved Relationship

Effects

  • 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

Challenges

  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

What?

  • 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’

Examples

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

  • Clinical trial and personalized medicine

  • Social media memes

  • Filter bubbles and echo chambers

Solutions

  • More classical statistics!

  • A better characterization of human needs and flaws

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!

Value

  • 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)

Solutions

  • 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

Marketing

  • 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?

Discrimination

  • 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

Marginalization

  • 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?

Amelioration

  • 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

Awareness

  • 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

Conclusion

  • Data science offers a great deal of promise
  • There are challenges and pitfalls
  • It is incumbent on us to avoid them

Many solutions rely on education and awareness

Thanks!