That doesn’t look right! – How to find the glitches in your data and models (anomaly detection, bias/fairness)

14:00 – 14:10    Julien Gruhier, Senior Manager Data Scientist at BT
Challenges of analysing mobile network data
The Geo-spatial analytics market is large and expanding every year. As a Telecommunication operator we can access part of this market relying primarily on the meta data of our mobile network. Despite a limited location accuracy compared with GPS based providers, we are nevertheless a strong contender. However, there are numerous data science challenges that need to be tackled to extract relevant insights from this wealth of information. I will touch on some of our core challenges to filter out noise from the signal to enable the extraction of data insights and help our customers to make data driven decisions.

14:10 – 14:15    Oliver Waring, Senior Data Science Specialist at BT
Q& A for “Challenges of analysing mobile network data”

14:15 – 14:30    Trevor Burbridge, BT

TBC

14:30 – 14:45    Kes Ward, Mathematics PhD student at Lancaster University

Anomaly Detection at the Edge

In an Internet of Things where everything is collecting and analysing its own data, we need edge analytics to help us sort the meaningful from the muck without breaking the computational bank. In this talk I will present a new statistical method for finding anomalies of different shapes and sizes in a real-time data signal, while working under extremely tight computational constraints.

14:45 – 15:00    Faisal Nazir and Subhash Talluri,  AWS at Amazon Web Services (AWS)

Faisal Nazir on “The importance of explainability of AI”

Data scientist could potentially wield great power over the lives of everyday people. This power comes from how they develop ML models that can be used to make life-changing decisions. Explainability – having knowledge of why an model makes an inference – is the field that tries make sense of a models decision. We will discuss what tooling is available to Data Scientists to help them find out what is going on with the models they train.

Subhash Talluri on “Data Science on AWS”

AWS has been continually expanding its service portfolio to support virtually any cloud workload, including many services and features in the area of artificial intelligence. In the context of data science projects on AWS, the benefits of cloud computing include agility, cost savings, elasticity, faster innovation and smooth transition from prototype to production. Amazon SageMaker is a fully managed offering that addresses every aspect of machine learning by its modular design. All machine intelligence is powered by data. However, not all data are created equal. We need to critically evaluate machine-learning products from a standpoint that prioritizes the quality of the data streaming into them. This necessitates the need for a data lake or a data platform with considerations

15:00 – 15:15    Ben Taylor, CTO Rainbird

Bias, transparency and governance in automated decision making

As they look at the great landscape of AI, organisations are getting to picture machine learning in finer detail. But the closer they get to the detail, the more they notice a chasm emerging between prediction and automated decision. And for no organisation does that chasm pose greater danger than those who operate in regulated industries.

15:15 – 15:30    Alex Healing, BT

TBC

15:30 – 15:45    Prof Sam Madden, MIT, Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory

TBC

15:45 – 16:30

Panel on ‘Data quality and data anomalies’ and Q&A

Sometimes the data we work with is not right. Some parts may be missing or some may be plain wrong. Data can misrepresent the world which leads to bias in models. How do we find what’s wrong in data and models – or what looks wrong but tells us something interesting?

  • Merve Alanyali Rafferty, Lead Data Scientist at LV
  • Kes Ward, Lancaster University
  • Prof Sam Madden, MIT
  • Ben Taylor, CTO Rainbird
  • Oliver Waring, BT
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Organiser: Tommy Flowers Network

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