As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today.
Do you want to try out some of the tools and applications that you heard about on Podcast.__init__? Do you have a side project that you want to share with the world? With Linode’s managed Kubernetes platform it’s now even easier to get started with the latest in cloud technologies. With the combined power of the leading container orchestrator and the speed and reliability of Linode’s object storage, node balancers, block storage, and dedicated CPU or GPU instances, you’ve got everything you need to scale up. Go to pythonpodcast.com/linode today and get a $100 credit to launch a new cluster, run a server, upload some data, or… And don’t forget to thank them for being a long time supporter of Podcast.__init__!
- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
- When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
- Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email email@example.com)
- To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
- Join the community in the new Zulip chat workspace at podcastinit.com/chat
- Your host as usual is Tobias Macey and today I’m interviewing Emily Miller and Peter Bull about Deon, an ethics checklist for data projects
- How did you get introduced to Python?
- Can you start by describing what Deon is and your motivation for creating it?
- Why a checklist, specifically? What’s the advantage of this over an oath, for example?
- What is unique to data science in terms of the ethical concerns, as compared to traditional software engineering?
- What is the typical workflow for a team that is using Deon in their projects?
- Deon ships with a default checklist but allows for customization. What are some common addendums that you have seen?
- Have you received pushback on any of the default items?
- How does Deon simplify communication around ethics across team boundaries?
- What are some of the most often overlooked items?
- What are some of the most difficult ethical concerns to comply with for a typical data science project?
- How has Deon helped you at Driven Data?
- What are the customer facing impacts of embedding a discussion of ethics in the product development process?
- Some of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced?
- What are your hopes for the future of the Deon project?
Keep In Touch
- Driven Data
- The Model Bakery in Saint Helena and Napa, California
- Driven Data
- International Development
- Brookings Institution
- Metis Bootcamp
- Podcast.__init__ Episode On Software Ethics
- Jupyter Notebook
- cookiecutter data science
- Logistic Regression