​Statistical Methods for Imputing Race & Ethnicity​

Date:

Friday, June 7, 2024

Time:

10:55 am

Room:

Phoenix Ballroom A

Summary:

Events in recent years have led to a fresh wave of discussions about racial justice and equality in the United States. This has led to an increased focus in the insurance industry and regulatory community on bias and equity. However, a lack of consistent data collection is often a significant barrier to the study of disproportionate impacts and equity across race/ethnicity cohorts in various contexts.

In this presentation, we describe a range of techniques for developing probabilistic estimates or predictions of individual race and/or ethnicity. We will show how to apply some of these methods to a simulated dataset to illustrate how to use them in practice. In addition, we will share results from a case study that assesses the predictive performance of these probabilistic estimates using an actual dataset from the insurance industry that has self-reported race/ethnicity recorded.

Automated Competitor Analysis Using Open Source & Third Party Tools

Date:

Friday, June 7, 2024

Time:

11:20 am

Summary:

Walkthrough an Automated Competitor Analysis setup that focuses on using inexpensive 3rd party and open source tools. This presentation will overview parts of a KNIME built workflow to monitor competitors and key content while storing the images of documented changes. Most monitoring services have “alert overload” where changes are code based, single worlds, etc and not indicative of a significant strategy change by the business. We use machine learning to train a model to only alert us to material changes and motivate actionable insights.

Ready to attend?

Register now! Join your peers.

Register nowView Agenda
Newsletter Knowledge is everything! Sign up for our newsletter to receive:
  • 10% off your first ticket!
  • insights, interviews, tips, news, and much more about Machine Learning Week
  • price break reminders