Actionable Ethics for Data Scientists
Actionable Ethics for Data Scientists

Abstract: 

There is a growing awareness of ethical concerns like algorithmic bias, yet current conversations around data ethics often focus solely on the importance of the problem without providing concrete ways to move forward. In this talk, Emily will provide an actionable approach that enables you as a data scientist to start identifying and addressing ethical concerns in your work today.

This approach to data ethics is based on integrating an ethics checklist into your existing data science workflow. We will be using deon (http://deon.drivendata.org), a lightweight, open-source command line tool that allows you to easily add an ethics checklist to your data science projects. Deon and the checklist framework enables you to actively consider the ethical implications of your work, and to preemptively address issues that may otherwise get overlooked. Rather than providing simple answers, this checklist spurs an ongoing dialogue that helps surface trade-offs, nuances, and unintended consequences.

Using the default checklist in deon, Emily will explain the relevant ethical concerns that arise at different points in the data science process — ranging from data collection to storage to modeling and deployment. She’ll illustrate these concerns with concrete examples of times where overlooking topics on the ethics checklist has caused unnecessary headache or harm. Using real stories of improperly hashed NYC taxi data, skewed training sets used in crime prediction, biased geometry in embedding spaces, and more, we will discuss a diverse set of ethical issues common in the course of data science work.

Ethics in data science requires more than just good intentions. Come learn how to jumpstart the conversation all data science teams should be having and translate those good intentions into ethical actions.

Bio: 

Emily Miller is a Data Scientist at DrivenData, where she helps mission-driven organizations leverage the power of data science and machine learning to maximize their impact. She is passionate about using data for social good and has previously worked at the Bill & Melinda Gates Foundation, Stanford Center for International Development, and Brookings Institution. She holds a master's in International Development from The New School and a data science certificate from Metis.