Catch the replay of Data Intersections 2020, and hear all the presentations and the panel discussion: Otavio Bueno, Heather Krause, Yeshimabeit Milner, Mike Monteiro, Panel Discussion with Ken Goodman. (share the playlist)

 

 

 

 

 

Speakers

Otávio Bueno

Professor and Chair, Philosophy Department, University of Miami
Cooper Senior Scholar in Arts and Science
Editor-in-Chief, Synthese
Editor-in-Chief, Synthese Library Book Series

Most of my work focuses on philosophy of science, philosophy of mathematics, and philosophy of logic. I have been trying to develop an empiricist view about science that is compatible with a nominalist view about mathematics and logic. Not an easy task, but one that has led me to explore also a number of fascinating and interrelated issues in metaphysics, epistemology and logic.

Read more . . .

Talk Title:  Data: Big and Small

Data need to be obtained, stored, displayed, analyzed, interpreted, and shared. At each stage, ethical and epistemological issues emerge, often in intertwined ways. In some cases, the data that is needed cannot be obtained because it is ethically unacceptable to do so; and what can be obtained does not settle a number of pressing issues. Once obtained, some data contain sensitive information that needs to be safely stored. But how can this be done responsibly and effectively? There are often different ways of displaying and analyzing the same data, which opens up different interpretations. How can the accuracy of such interpretations be assessed and determined? Finally, data need to be shared in responsible ways, but often there are distinct mechanisms of disseminating the data, and misleading ways of doing so. In this talk, I will develop a framework to think about data, whether big or small, and consider these issues.

 

 

Heather Krause

Founder of Datassist and We All Count

Heather Krause, PStat, is a data scientist with over a decade of experience building tools that improve practices and systems. Heather is a statistician with years of experience working on complex data problems and producing real world knowledge. She has a strong love of finding data, analyzing it in creative ways and using cutting edge visualization methods to visualize the results. Her emphasis is on combining strong statistical analysis with clear and meaningful communication. She is currently working on implementing tools for equity and ethics in data. As the founder of two successful data science companies, she attacks the largest questions facing societies today, working with both civic and corporate organizations to improve outcomes and lives. Her relentless pursuit of clarity and realism in these projects pushed her beyond pure analysis to mastering the entire data ecosystem including award winning work in data sourcing, modelling, and data storytelling, each incorporating bleeding edge theory and technologies.

Her work proves that data narratives can be meaningful to any audience from a boardroom to the front page. Heather is the founder of We All Count, a project for equity in data working with teams across the globe to embed a lens of ethics into their data products from funding to data collection to statistical analysis and algorithmic accountability. Her unique set of tools and contributions have been sought across a range of clients from MasterCard and Wells Fargo to the United Nations, the Canadian Government and the Bill and Melinda Gates Foundation. She is on the Data Advisory Board of the UNHCR.

Talk Title:  How not to use data like a racist, sexist, homophobe (etc.  . . .)  A seven-step framework for ethics and equity in data

Very few people build data products because they want to promote racist or sexist ideas. However, it’s very easy to accidentally fall into these traps, particularly when there’s so much talk about the objectivity of evidence. It’s really easy to make mistakes when using data. A working understanding of how to incorporate ethics and equity into data products is essential for anyone conducting data analysis or making decisions based on data analysis. This talk provides you with several shocking real-world examples and a seven-step framework for identifying inequity and hidden bias in the data product lifecycle. As interest in ethics in data grows, this remains one of the few actionable frameworks for making equitable change in the way you use data as a team. It has been successfully implemented to improve ethics within data, algorithms, dashboards and more at Mastercard, Oxfam, the UN, the Margaret Cargill Foundation, Borealis, and several national governments.

 

Yeshimabeit Milner

Founder of Data for Black Lives
2017 Black Male Achievement Fellow

Yeshimabeit Milner is the Founder & Executive Director of Data for Black Lives. She has worked since she was 17 behind the scenes as a movement builder, technologist and data scientist on a number of campaigns. She started Data for Black Lives because for too long she straddled the worlds of data and organizing and was determined to break down the silos to harness the power of data to make change in the lives of Black people. In two years, Data for Black Lives has raised over $2 million, hosted two sold out conferences at the MIT Media Lab and has changed the conversation around big data & technology across the US and globally.

As the founder of Data for Black Lives, her work has received much acclaim. Yeshimabeit is an Echoing Green Black Male Achievement Fellow, an Ashoka Fellow and joins the founders of Black Lives Matter and Occupy Wall Street in the distinguished inaugural class of Roddenberry Foundation Fellows. Most recently she was named, along with Data for Black Lives co-founder Lucas Mason-Brown, one of Forbes 30 Under 30. Yeshimabeit has a BA from Brown University and serves on the board of the historic Highlander Center for Research & Education.

Talk Title:  Abolish Big Data

Big Data is more than a collection of technologies, more than a revolution in measurement and prediction. It has become a philosophy, an ideological regime, one that determines how decisions are made and who makes them. It has given legitimacy to a new form of social and political control, one that has taken the digital traces of our existence and found ways to use them to sort and manage populations. Big Data is part of a long and pervasive historical legacy of scientific oppression, aggressive public policy, and the most influential political and economic institution that has and continues to shape this country’s economy: chattel slavery. Algorithms and other data technologies are the engines that have facilitated the ongoing evolution of chattel slavery into the Prison Industrial Complex, justified the militarization of schoolyards and borders alike, and continued the expansion of contemporary practices of peonage. This talk serves as a call to action to reject the concentration of Big Data in the hands of a few, to challenge the structures that allow data to be wielded as a weapon of immense political influence. To abolish Big Data would mean to put data in the hands of people who need it the most.

 

Mike Monteiro

Co-Founder, Design Director | Mule Design

Mike Monteiro is the co-founder and design director of Mule Design. He prefers that designers have strong spines. Mike writes and speaks frequently about the craft, ethics, and business of design. His most recent book is Ruined by Design: How Designers Destroyed the World, and What We Can Do to Fix It. He is also the author of Design is a Job and You’re My Favorite Client, both from A Book Apart. Mike received the 2014 Net award for Conference Talk of the Year for his inspirational polemic on responsibility, “How Designers Destroyed the World.”

Talk Title:  The Smallest Sliver of Hope

We built the engine that broke the world. We built the machines that turned vitriol into profit for bankers and billionaires. And we turned a blind eye because of the crumbs falling from their table. But as the window closes on humanity, and we finally realize the error of our ways, do we have a shot? Can we turn things around? Is there even the smallest sliver of hope? Possibly. But you’re not gonna like it.