AI4ER Lecture: Projects

Neil D. Lawrence

Wolfson Lecture Theatre, Madingley Rise Site

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’

Embodiment Factors

bits/min billions 2,000
billion
calculations/s 		~100 a billion
embodiment 20 minutes 5 billion years

Evolved Relationship with Information

New Flow of Information

Evolved Relationship

Evolved Relationship

The Gartner Hype Cycle

Cycle for ML Terms

Challenges

  1. Paradoxes of the Data Society
  2. Quantifying the Value of Data
  3. Privacy, loss of control, marginalization

Challenges

  1. Paradoxes of the Data Society

  2. Quantifying the Value of Data

  3. Privacy, loss of control, marginalization

Privacy, Loss of Control and Marginalization

  • Society is becoming harder to monitor
  • Individual is becoming easier to monitor

Digital Revolution and Inequality

  • 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
  • GDPR: General Data Protection Regulation

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
  • GDPR: Good Data Practice Rules

Marginalization

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

Digital Revolution and Inequality?

Data Science Africa is a bottom up initiative for capacity building in data science, machine learning and AI on the African continent

Example: Prediction of Malaria Incidence in Uganda

Martin Mubangizi Ricardo Andrade Pacecho John Quinn

Malaria Prediction in Uganda

(Andrade-Pacheco et al., 2014; Mubangizi et al., 2014)

Tororo District

Malaria Prediction in Nagongera (Sentinel Site)

Mubende District

Malaria Prediction in Uganda

GP School at Makerere

Kabarole District

Early Warning System

Early Warning Systems

Crop Monitoring

Ernest Mwebaze

Ernest’s Talk

You can see more details on Ernest’s work in this talk from the 2021 DNN lectures.

Biosurveillance

Martin Mubangizi

Community Radio

Morine Amutorine

Kudu Project

Safe Boda

Feudal Era Data Ecosystem

Information Barons threaten our Privacy

African Data Sharing Covid-19

Morine Amutorine
Jessica Montgomery Victor Ohuruogu

References

Thanks!

Andrade-Pacheco, R., Mubangizi, M., Quinn, J., Lawrence, N.D., 2014. Consistent mapping of government malaria records across a changing territory delimitation. Malaria Journal 13. https://doi.org/10.1186/1475-2875-13-S1-P5
Lawrence, N.D., 2017. Living together: Mind and machine intelligence. arXiv.
Mubangizi, M., Andrade-Pacheco, R., Smith, M.T., Quinn, J., Lawrence, N.D., 2014. Malaria surveillance with multiple data sources using Gaussian process models, in: 1st International Conference on the Use of Mobile ICT in Africa.