Typically a series of digital audio recordings on a particular topic that are shared and made available through mobile devices or computers.
Author: N/A; Publisher: Linear Digressions; Publication Year: 2020. The following podcast episode introduces the idea of personal identifiable information (PII) in reference to census data and weighs the importance of the granularity of data against the right to privacy. In the case of census data, it is critical to the functioning of our government, but in the case of undocumented people represents a threat if the data is…
Author: N/A; Publisher: Linear Digressions; Publication Year: 2020. The following podcast episode discusses how regardless of whether race or ethnicity is included in your algorithm, correlated variables can still instill biases. The variables selected may lead to more accurate models, but there are ethical scenarios in which you should not be using them. Biases in society are reflected in data: your algorithm results…
Author: N/A; Publisher: Linear Digressions; Publication Year: 2020. The following podcast episode discusses how the quality of data around policing is hit-and-miss based on region. Reporters of data are those being scrutinized, so there is some self-reporting bias. Human biases are reflected in the data, so it is important to draw conclusions carefully…
Author: Kyle Polich, Scott Nestler; Publisher: Data Skeptic; Publication Year: 2018. In the following podcast episode, Kyle and Scott discuss that there is not currently a single, industry wide framework for ethics in data science as it is a new, growing field. Likewise, the topic of ethics in data science is also ever evolving. They compare the field to others, such as law and policy making as well as journalism to identify whether these should…
Author: Kathleen Walch, Ronald Schmelzer; Publisher: AI & Data Today; Publication Year: N/A. The following podcast focusses on how building frameworks can help avoid the pitfalls associated with bias and fairness in artificial intelligence (AI). Unchecked, we could use algorithms to reinforce biases in the real world. Inclusivity is key: build your algorithms with the target audience in mind, but mind your edge cases as people are not one-size fits…
Author: Margo Gerritsen, Karen Hao; Publisher: Women in Data Science Podcast; Publication Year: N/A. In the following podcast episode, the focus is on Karen Hao who wrote an influential article in MIT Technology Review describing ethical tensions at Facebook. In this segment of the interview, Karen describes the conflict between Facebook’s need to grow and the artificial intelligence algorithm’s amplification of stories that contained misinformation…
Author: James Hodson, Jon Krohn; Publisher: Super Data Science; Publication Year: 2021. The following podcast episode is an interview with James Hodson who founded AI for Good. He started the organization to focus on using artificial intelligence (AI) to help achieve sustainable development goals (SDGs) that were agreed upon by 196 countries as some of the most important issues of the 21st century. The organization is focused on…
Author: Hiwot Tesfaye, Greg; Publisher: SAS; Publication Year: 2022. The following podcast episode discusses how algorithms in healthcare are often used for accessing risk. Pitfalls come from not understanding how the healthcare system has worked historically and how different populations interact with healthcare systems. Accuracy of algorithms at a global level and the individual subpopulation level is discussed as well. It is…
Author: Ezra Klein, C. Thi Nguyen; Publisher: The Ezra Klein Show; Publication Year: 2022. The following podcast episode touches on several topics related to data and ethics. It notably focuses on the increasing role of gamification in society and the effects that using data to influence choices has on personal agency. This includes optimizing for engagement and manipulating incentive structures to get consumers “hooked” on products…
Author: Data Science Ethics Podcast
Publisher: Spotify
Publication Year: 2020
Summary: The following podcast episode discusses how gender and racial gaps exist in the field of data science as it tends to be dominated by certain groups such as white men. While we understand that inclusivity and diversity are essential for ethical data science practices, constraints sometimes prevent teams from having a wide spectrum of backgrounds (e.g…