retain reponsibility
Retain Responsibility

Show Notes on Retain Responsibility One of the core tenets of ethical behavior in data science revolves around the concept of needing to retain responsibility or accountability. A differentiator between our take on this and that most commonly conveyed is the distinction between the two terms. Why, then, do we use the term “responsibility” instead […]

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Collect Carefully

Episode 28: Collect Carefully – Show Notes The era of Big Data has meant the ability gathering and processing of vast stores of information about almost anything. It enables data scientists to bring enormous swaths of data to bear on a given problem. Further, it expands the ability to collect data from research techniques that […]

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Protect Privacy

Episode 22: Protect Privacy – Show Notes IT are not the only ones responsible to protect privacy of data. Data scientists share this burden as they search for, collect, store, utilize, and share vast amounts of information. In this episode, we explore what data scientists and non-practitioners should do to help protect privacy. Additional Links […]

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Proxy Variables

Episode 19: Proxy Variables – Show Notes This quick, informational segment introduces the concept of proxy variables. In short, proxy variables are data elements used in place of something that may be more pertinent but also more difficult to measure. It also touches on confounding and lurking variables – in case you wanted a dose […]

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Train Transparently

Episode 18: Train Transparently – Show Notes As algorithms are created and unleashed upon the world, it is crucial to understand not only what they are but how they came to be. The best way to accomplish this before chaos is wreaked is to train transparently – meaning to let people know what is going […]

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Become a Member

Hi listeners. This is Lexy with the Data Science Ethics Podcast. Today, I’ve got a special announcement. We are gearing up to launch the members only podcast! As we teased in the introductory episode, the members only podcast explores data science ethics in pop culture. Our first topic is an episode of Star Trek: The […]

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Anticipate Adversaries

Episode 10: Anticipate Adversaries – Show Notes Adversaries to an algorithm or system can come in many guises and at many times in the data science process. Their contexts vary from well-intentioned to nefarious. In this episode, we talk about different types of adversaries and how to anticipate adversaries. Well-Intentioned Adversaries Business users who have […]

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Consider Context in Data Science
Consider Context

Episode 9: Consider Context – Show Notes Algorithms do not operate in a vacuum. They operate in the context of a specific business, industry, problem, group of people, time frame, and more. Algorithms impact people and processes in the course of their use. The charge of an ethical data scientist is to develop solutions which […]

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Anchor on blue wall
Anchoring Bias

Episode 7: Anchoring Bias – Show Notes Today we are talking about one aspect of why first impressions are so important – anchoring bias. Anchoring bias is when the first piece of information we receive about something (our first impression of ir) is weighted more heavily than it should be. Unlike statistical sampling biases, anchoring […]

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Statistical bias in sampling
Statistical Sampling Bias

Episode 4: Statistical Sampling Bias – Show Notes Bias sneaks in to algorithms and data science from multiple sources. Primarily, it comes from statistical or cognitive biases that then lead to biased conclusions or results. In today’s episode, we look at four types statistical sampling bias to understand how biased samples skew algorithms. [2:54] Selection […]

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