Of Checklists, Ethics, and Data with Emily Miller and Peter Bull - Episode 184

Summary

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.

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Preface

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  • 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

Interview

  • Introductions
  • 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?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Notable Replies

  1. A question for the guests. You didn’t go into more detail about determining how exactly
    do you know that the insight coming out of a model or a data set is wrong,
    other than the subjective judgment of the researcher.

    And that’s something I’d like to know more about, since I haven’t seen it explained
    a lot. Getting rid of biases seems undeniably good if they make the system
    perform worse. But isn’t deciding about what is biased/inaccurate based on personal
    opinions also biased and inaccurate?

    Let’s say that we come up with research that perpetuates some stereotype.
    It’s determining the validity of that finding an issue of gaining more data
    or doing more statistical analysis, not whether the researcher agrees with the results?
    Science and truth are impersonal, right? So where exactly is the place for ethics in here,
    which so far don’t seem to be one of humanity’s solved problems? :slight_smile:

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Participants