Harvard Data Science Review Podcast

Data Science for Criminal Justice: Can We Avoid Black Box Algorithms for High-Stake Decisions?

In this episode we examine the use of secret or black box algorithms for high-stake decisions, particularly in the criminal justice system. How do they factor in the decisions made every day by state and federal courts concerning bail, sentencing, and parole? Are black box algorithms fair and unbiased? Do they help counteract or support societal prejudices?  Is their use in criminal justice cases serving the public’s best interest?

We discuss these issues and more with two experts on the topic: Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics and Director of the Interpretable Machine Learning Lab at Duke University and Brandon Garrett, Professor of Law and founder of the Wilson Center for Science and Justice at Duke University.

Can Data Science Help the Wine Industry (and me, to pick up a good bottle)?

‘Tis the season to be merry and bright, and doesn’t a glass of wine go a long way to help ring in the festivities?  We think so because this month's episode of the HDSR Podcast is devoted to the wine industry – from production to consumption.  We discuss the impact of COVID on the global supply chain, the idiosyncrasies of local government regulations, the effects climate change is having on traditional and emerging grape growing regions, how winemakers use data analysis, and most important, what are the key data points that every potential wine connoisseur should know in order to impress their families and friends at holiday gatherings this season.

We are joined by two expert guests: Orley Ashenfelter, the Joseph Douglas Green 1895 Professor of Economics at Princeton University and President of the American Association of Wine Economists and Don St Pierre, the Executive Chairman of Vinfolio, the U.S.’s leading fine wine marketplace, investment advisor, and professional wine storage facility.  

Government Data: How Do They Serve Us but Also Concern Us

On this episode we examine how the U.S. government collects data to serve the public and how to ensure such a process does not hurt the people it aims to serve. We discuss the good, the bad, and the ugly sides of this topic including privacy issues, the 2020 U.S. Census, how well-intended methods may adversely affect minority populations, and why it’s important for local communities to collect and report their own data. We also ask how transparent should the federal government be about its data collection and who should the public be most worried about when it comes to data privacy?

Our guests are Tim Persons, Chief Scientist for the U.S. Government Accountability Office (GAO) and Managing Director of its Science, Technology Assessment, and Analytics team and Julia Lane, New York University professor and co-founder of the Coleridge Initiative, a not-for-profit organization that is working with governments to ensure that data are more effectively used for public decision-making.

Pollsters: The Discoverers and Guardians of Public Opinion

This episode focuses on the art and science of measuring public opinion. We discuss the challenges pollsters face when trying to predict how public opinion may change over time, review both the innovative and time-tested methods of polling and discover which recent polls have revealed the most surprising data. 

Our guests are Kristen Soltis Anderson, pollster, speaker, commentator, author and co-founder of Echelon Insights, an opinion research and analytics firm; and Cliff Young, President of US Public Affairs at Ipsos, Adjunct Lecturer at Johns Hopkins University and a frequent writer, analyst, and commentator on elections, electoral polling, and public opinion.

The Future of Artificial Intelligence: Will it be the Terminator or the Jetsons?

On this episode we explore all things AI with our guests Kathleen Walch and Ron Schmelzer, hosts of the popular AI Today podcast and principal analysts and managing partners of Cognilytica, an AI research and advisory firm.

With Kathleen and Ron, we discuss the spread of AI in our lives, from autonomous vehicles to Taco Bell’s new automatic drive thru lanes. But has too much been promised and not delivered? Are we on the brink of an AI winter, where development and investment cool down? We look at all the possibilities of how our future will change with AI. 

Our hosts, Xiao-Li and Liberty were also guests on the AI Today podcast. If you’d like to hear that interview, you can listen here: https://www.cognilytica.com/2021/09/16/ai-today-podcast-interview-with-harvard-data-science-review-hdsr-podcast-hosts-liberty-vittert-xiao-li-meng/

Healthcare Data: Who Takes Care of it and How Healthy is it?

Over 30% of the world’s data is comprised of healthcare data with the U.S. government arguably collecting the largest portion. On this month’s episode of the Harvard Data Science Review Podcast, we explore all things healthcare data with the help of two experts who provide their perspectives from the public and private sectors.  Our guests are Justin Fanelli, Chief Architect of Defense Medical Intelligence Data and the Technical Director at the Naval Information Warfare Center and Michelle Holko, Principal Architect Public Sector Cloud for Healthcare and Life Sciences at Google.

Mental Health Challenges: How Can Data Science Help?

This month’s episode focuses on the increasing role of data science in the diagnosis and treatment of mental health disorders.  It explores how statistical tools like adaptive testing are being successfully deployed to rapidly identify people with high levels of depression, anxiety or suicide risk. It also examines how the data science community could further improve its efforts to support mental health research and policymaking.

Our guests are Margarita Alegria, Chief of the Disparities Research Unit, Massachusetts General Hospital and Professor, Harvard Medical School and Robert Gibbons, Professor of Biostatistics, Departments of Medicine, Public Health and Psychiatry, University of Chicago and author of Medications and Suicide: High Dimensional Empirical Bayes Screening (iDEAS) for HDSR.

 

Are you Disinformed or Misinformed?

In this episode, Harvard Data Science Review digs into the world of disinformation and misinformation, and the difference between them. Is the weaponization of both a new phenomenon or is history repeating itself? How has social media and the democratized access to published information contributed to today’s sensationalized headlines?  Hosts Xiao-Li Meng and Liberty Vittert explore these questions and more with the help of two experts on the topic, Scott Tranter, CEO and founder of Optimus Analytics and Hany Farid, Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering & Computer Science and the School of Information.  

The Art and Value of Machine Learning in Valuing Art: Hype or Hope?

What is the value of art? Is it in the eye of the beholder or can data analytics tools place a monetary value on beauty? Hosts Xiao-Li Meng and Liberty Vittert explore the use of data and technology in the art world with guests Jason Bailey, CEO and founder of Artnome and author of “Can Machine Learning Predict the Price of Art at Auction?” for HDSR and  Dan Cameron, American art curator, writer, and educator.

Predicting (2021) Oscar Winners: How Crystal is the Statistical Ball?

What are the biggest predictors of an Oscar win?  What are the pros and cons of using quantitative vs. qualitative data? Has the film industry’s increased use of streaming services impacted Oscar predictions? Hosts Xiao-Li Meng and Liberty Vittert investigate these questions and more by speaking with two renowned Oscar awards predictors: Boston Globe film critic and columnist Ty Burr and Ben Zauzmer, author of Oscarmetrics: The Math Behind the Biggest Night in Hollywood and the HDSR article, "Oscar Seasons: The Intersection of Data and the Academy Awards".