A Data Science Take on Open Policing Data

Linear Digressions

#Data Science #Science #Data #Linear #Digress #Ben Jaffe and Katie Malone #Ben Jaffe #Katie Malone #Tech #Machining #Learn

32:08

Did you know there’s an actual science to uncovering your hidden genius? It’s not about filling out a “dream job” worksheet—it’s about understanding how your brain is wired, identifying your natural aptitudes, and using them to thrive. This isn’t just a self-discovery exercise. It’s a game-changer for your career, your relationships, and how you show up in the world. Betsy Wills and Alex Ellison are redefining how we approach career discovery, proving that finding the right path isn’t just about landing a job—it’s about creating a life that aligns with who you actually are. ✅ Betsy Wills – Cofounder of YouScience, a groundbreaking psychometric assessment platform reshaping how we understand our talents. She’s also the Director of Marketing & Branding at Diversified Trust and a frequent lecturer at Vanderbilt University and NYU’s Stern School of Business. ✅ Alex Ellison – Founder of Throughline Guidance, a global college and career counseling practice. She’s a sought-after writer, speaker, and expert in college readiness and career development. ✅ Together, they co-authored Your Hidden Genius: The Science-Backed Strategy to Uncovering and Harnessing Your Innate Talents. Discovering your hidden genius isn’t just about career success—it’s about tapping into what makes you, you . Connect with Betsy & Alex: Website (Free Downloads): www.yourhiddengenius.com Book: https://www.harpercollins.com/products/your-hidden-genius-elizabeth-m-willsalexandra-ellison Related Podcast Episodes: How To Be You, But Better with Olga Khazan | 288 Finding Purpose Through Human Design with Emma Dunwoody | 228 195 / Finding (And Using) Your Voice with Amy Green Smith Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…

hace 5 años 23:44

MP3•Episodio en casa

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

293 episodios

A Data Science Take on Open Policing Data

Linear Digressions

3,118 subscribers

published hace 5 años

MP3•Episodio en casa

293 episodios

#Data Science #Science #Data #Linear #Digress #Ben Jaffe and Katie Malone #Ben Jaffe #Katie Malone #Tech #Machining #Learn

All episodes

1
So long, and thanks for all the fish 35:44

hace 5 years35:44

35:44

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years. It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night! —Katie and Ben…

1
A Reality Check on AI-Driven Medical Assistants 14:00

hace 5 years14:00

14:00

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.…

1
A Data Science Take on Open Policing Data 23:44

hace 5 years23:44

23:44

1
Procella: YouTube's super-system for analytics data storage 29:48

hace 5 years29:48

29:48

This is a re-release of an episode that originally ran in October 2019. If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”…

1
The Data Science Open Source Ecosystem 23:06

hace 5 years23:06

23:06

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level. https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2…

1
Rock the ROC Curve 15:52

hace 5 years15:52

15:52

This is a re-release of an episode that first ran on January 29, 2017. This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

1
Criminology and Data Science 30:57

hace 5 years30:57

30:57

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal justice, but wanting to be data-driven about it, this conversation with Zach is a must-listen.…

1
Racism, the criminal justice system, and data science 31:36

hace 5 years31:36

31:36

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fairness” of the predictions and what is causing its racially disparate output (to wit: race is explicitly not an input to the algorithm, and yet the algorithm gives outputs that correlate with race—what gives?) Unfortunately it’s not an open-and-shut case of a tuning parameter being off, or the wrong metric being used: instead the biases in the justice system itself are being captured in the algorithm outputs, in such a way that a self-fulfilling prophecy of harsher treatment for black defendants is all but guaranteed. Like many other things this week, this episode left us thinking about bigger, systemic issues, and why it’s proven so hard for years to fix what’s broken.…

1
An interstitial word from Ben 5:59

hace 5 years5:59

5:59

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

1
Convolutional Neural Networks 21:55

hace 5 years21:55

21:55

This is a re-release of an episode that originally aired on April 1, 2018 If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.…

1
Stein's Paradox 27:02

hace 5 years27:02

27:02

This is a re-release of an episode that was originally released on February 26, 2017. When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group? The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.…

1
Protecting Individual-Level Census Data with Differential Privacy 21:19

hace 5 years21:19

21:19

The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be ways to re-identify the records or otherwise figure out sensitive personal information. That problem has motivated the study of differential privacy, a set of techniques and definitions for keeping personal information private when datasets are released or used for study. Differential privacy is getting a big boost this year, as it’s being implemented across the 2020 US Census as a way of protecting the privacy of census respondents while still opening up the dataset for research and policy use. When two important topics come together like this, we can’t help but sit up and pay attention.…

1
Causal Trees 15:27

hace 5 years15:27

15:27

What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists Susan Athey and Guido Imbens are on the case. This episodes explores their algorithm for recursively partitioning a dataset to find heterogeneous treatment effects, or for you ML nerds, applying decision trees to causal inference problems. It’s not a free lunch, but for those (like us!) who love crossover topics, causal trees are a smart approach from one field hopping the fence to another. Relevant links: https://www.pnas.org/content/113/27/7353…

1
The Grammar Of Graphics 35:38

hace 5 years35:38

35:38

You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying data using just visual cues. It’s a bit abstract but very profound, and these principles underlie the ggplot2 package in R that makes famously beautiful plots with minimal code. This episode covers a paper by Hadley Wickham (author of ggplot2, among other R packages) that unpacks the layered approach to graphics taken in ggplot2, and makes clear the assumptions and structure of many familiar data visualizations.…

1
Gaussian Processes 20:55

hace 5 years20:55