Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

156 | Catherine D’Ignazio on Data, Objectivity, and Bias

Jul 19, 2021

01:28:13

Snipd AI

Explore how biases creep into data and algorithms with Catherine D’Ignazio. Learn about the complexities of data classification, risks of misleading data visualization, and the need to address systemic discrimination in data science. Delve into feminist theories on objectivity and the importance of inclusive perspectives in scientific research. Discover how societal norms and prejudices impact human flaws and biases, and the power of data in promoting positive change.

AI Summary

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Data can inherit biases from collectors and analysts, impacting algorithms and objectivity.

Achieving true objectivity in data science is challenging due to human biases.

Ethical considerations are crucial in data collection to avoid perpetuating biases and discrimination.

Socio-cultural factors influence data collection practices, requiring a nuanced and inclusive approach.

Deep dives

Bias in Data Collection and Analysis

Data scientists and researchers need to be mindful of the biases that can seep into algorithms and data sets, even during the collection phase. This bias can arise from a variety of factors, such as the choice of data sets, the exclusion of certain data points, and the design of algorithms. Examples include facial recognition systems that are trained predominantly on white faces, leading to inaccuracies in recognizing faces with different racial characteristics. This highlights the importance of considering the diversity and inclusivity of data collection processes.

Introduction

5min

Exploring the Intersection of Data Feminism and the Complexity of Data Collection

16min

Analysis of Misleading Data Visualization in Media

3min

Unveiling Biases in Data and Algorithms

17min

Exploring Objectivity and Bias through a Feminist Lens

3min

Redefining Objectivity and Bias in Scientific Practices

18min

Exploring Language Learning and Human Flaws

2min

Navigating Bias, Inequality, and Intersectionality in Research

26min

How can data be biased? Isn’t it supposed to be an objective reflection of the real world? We all know that these are somewhat naive rhetorical questions, since data can easily inherit bias from the people who collect and analyze it, just as an algorithm can make biased suggestions if it’s trained on biased datasets. A better question is, how do biases creep in, and what can we do about them? Catherine D’Ignazio is an MIT professor who has studied how biases creep into our data and algorithms, and even into the expression of values that purport to protect objective analysis. We discuss examples of these processes and how to use data to make things better.

Support Mindscape on Patreon.

Catherine D’Ignazio received a Master of Fine Arts from Maine College of Art and a Master of Science in Media Arts and Sciences from the MIT Media Lab. She is currently an assistant professor of Urban Science and Planning and Director of the Data+Feminism Lab at MIT. She is the co-author, with Lauren F. Klein, of the book Data Feminism.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export