Data Science for Good Webinar

We want to invite you to participate in ODSC Data Science for Good Webinar!
We will discuss how can we leverage data science and its versatile tools to solve real-life problems. Technologies progress and develop, data becomes more prolific and useful. How can we, as data scientists benefiting from this momentum, help the rest of the world catch up?

To access this webinar, please register using the link below:
https://attendee.gotowebinar.com/register/560915152644257027

We are bringing 4 speakers to present 30 minutes sessions.
Date: Dec 19th
Time: 2 - 4 pm EST (11 am - 13pm PT)

Agenda Detail:

Session 1 - Data wrangling to provide solar energy access across Africa (30 Minutes)

Speaker:
Brianna Schuyler, Ph.D.

Abstract:
More than 600 million people in Sub-Saharan Africa have no access to electricity, and no documented financial history. A family can light their home and keep necessary electronics (such as a cell phone) charged using a small solar panel and battery, but most solar devices are not affordable to a vast number of people making $2 a day or less.

One solution to this problem is offering solar energy kits on a Pay As You Go basis, providing financial loans to families until they are able to pay off the cost of their device. However, people with severely restricted income oftentimes exhibit sporadic payment behavior which poses an interesting prediction problem. This rich and unique dataset can be used to develop credit profiles for individuals, allowing them access to credit for other life-changing loans or utilities.

In addition to financial information, the solar devices themselves send millions of bits of information regularly using a GSM chip. Information transferred through GSM, along with the financial data amassed through loan repayment, provide a fascinating dataset on which to model and explore. Data analysis and machine learning techniques allow increased energy access to those for whom the costs of solar were previously prohibitive, as well as increased adoption of renewable energy sources in a rapidly growing population.

Session 2 - Detecting semantic bias through interpretability (30 Minutes)

Speaker: Eric Schles

Abstract:
In this session, we will juxtapose classical statistical interpretability techniques against cutting-edge techniques. We will show how these newer techniques allow us to interpret models like neural networks, ensembles and support vector machines. The two main new tools we will use are SHAP and LIME.

We will apply this to data synthetic datasets, showing how one could detect semantic bias (non-statistical bias).

Session 3 - AI Ethics: Current Challenges

Speaker: Abhishek Gupta

Abstract:
This talk will highlight some of the emerging challenges when it comes to the responsible and ethical development and deployment of AI. It will use recent examples to illustrate some of the challenges and present potential strategies on how to best mitigate these issues. The talk will also highlight 2 projects coming up from the Montreal AI Ethics Institute that are aiming to concretely address some of these challenges.

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