AI is Disadvantaging Job Applicants, But You Can Fight Back

In today's important episode, the author, professor and journalist Hilke Schellmann details how specific HR-tech firms misuse A.I. to facilitate biased hiring, promotion, and firing decisions. She also covers how you can fight back and how A.I. can be done right!

Hilke’s book, "The Algorithm: How A.I. Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now", was published earlier this month. In the exceptionally clear and well-written book, Hilke draws on exclusive information from whistleblowers, internal documents and real‑world tests to detail how many of the algorithms making high‑stakes decisions are biased, racist, and do more harm than good.

In addition to her book, Hilke:

• Is Assistant Professor of Journalism and A.I. at New York University.

• Previously worked in journalism roles at The Wall Street Journal, The New York Times and VICE Media.

• Holds a Master’s in investigative reporting from Columbia University.

Today’s episode will be accessible and interesting to anyone. In it, Hilke details:

• Examples of specific HR-technology firms that employ misleading Theranos-like tactics.

• How A.I. *can* be used ethically for hiring and throughout the employment lifecycle.

• What you can do to fight back if you suspect you’ve been disadvantaged by an automated process.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Data Science Trends for 2023

Happy New Year! To kick it off, the entrepreneur, futurist, and mega-popular Machine Learning instructor Sadie St. Lawrence joins me to predict the biggest data science trends of 2023 🍾

We start the episode off by looking back at how our predictions for 2022 panned out from a year ago and then we dive into our predictions for the year ahead. Specific trends we discuss include:
• Data as a product
• Multimodal models
• Decentralization of enterprise data
• A.I. policy
• Environmental sustainability

This episode will appeal to technical and non-technical folks alike — anyone who’d like to understand the trends that will shape the field of data science and the broader world not only in 2023 but also in the years beyond.

Sadie:
• Has created data science and ML courses enjoyed by 350k+ students.
• Is Founder and CEO of Women In Data, a community of over 20k women across 17 countries.
• Serves on multiple start-up boards.
• Hosts the Data Bytes podcast.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Simplifying Machine Learning

Today, Mariya Sha — host of the wildly popular "Python Simplified" YouTube channel (140k subscribers!) — taps her breadth of A.I. expertise to provide a fun and fascinating finale to SuperDataScience guest episodes for 2022.

Mariya:
• Is the mind behind the "Python Simplified" YouTube channel that makes advanced concepts (e.g., ML, neural nets) simple to understand.
• Her videos cover Python-related topics as diverse as data science, web scraping, automation, deep learning, GUI development, and OOP.
• Is renowned for taking complex concepts such as gradient descent or unsupervised learning and explaining them in a straightforward manner that leverages hands-on, real-life examples.
• Is pursuing a bachelor's in Computer Science (with a specialization in A.I. and Machine Learning) from the University of London.

Today’s episode should appeal to anyone who’s interested in or involved with data science, machine learning, or A.I.

In this episode, Mariya details:
• How the incredible potential of ML in our lifetimes inspired her to shift her focus from web-development languages like JavaScript to Python.
• Why automation and web scraping are critical skills for data scientists.
• How to make learning any apparently complex data science concept straightforward to comprehend.
• Her favorite Python libraries and software tools.
• One rarely-mentioned topic that every data scientist would benefit from.
• The pros and cons of pursuing a 100% remote degree in computer science.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Equality Machine

Many recent books and articles spread fear about data collection and A.I. Today's guest, Prof. Orly Lobel, offers the antidote with her book "The Equality Machine" — an optimistic take on the future of data science.

Causal Machine Learning

Causal ML is today's focus with Dr. Emre Kiciman — Senior Principal Researcher at Microsoft, developer of the DoWhy causal modeling library for Python, and a leader in applying causal research to social sciences.

Emre:
• Has worked within prestigious Microsoft Research for over 17 years.
• Leads Microsoft’s research on Causal Machine Learning.
• Leads development of the DoWhy open-source causal modeling library for Python (part of the PyWhy GitHub project).
• Pioneered the use of social media data to answer causal questions in the social sciences, such as with respect to physical and mental health.
• Has published 100+ papers and been cited 8000+ times.
• Holds a PhD in Computer Science from Stanford University.

Today’s episode is relatively technical, so will probably appeal primarily to folks with technical backgrounds like data scientists, ML engineers, and software developers.

In this episode, Emre details:
• What Causal ML is and how it’s different from "correlational" ML.
• The four key steps of causal inference and how they impact ML.
• The types of data that are most amenable to causal methods and those that aren’t yet… but may be soon.
• Exciting real-world applications of Causal ML.
• The software tools he most highly recommends.
• What he looks for in the data science researchers he hires.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Open-Ended A.I.: Practical Applications for Humans and Machines

In today's remarkable episode, Dr. Kenneth Stanley uses evidence from his machine learning research on Open-Ended A.I. and evolutionary algorithms to inform how you as a human can achieve great life outcomes.

Ken:
• Co-authored the book "Why Greatness Cannot be Planned", a genre-defying book that leverages his ML research to redefine how a human can optimally achieve extraordinary outcomes over the course of their lifetime.
• Was until recently Open-Endedness Team Leader at OpenAI, one of the world’s top A.I. research organizations.
• Led Core A.I. Research for Uber A.I.
• With Prof. Gary Marcus and others, founded A.I. startup Geometric Intelligence, which was acquired by Uber.
• Was Professor of Computer Science at the University of Central Florida.
• Holds a dozen patents for ML innovations, including open-ended and evolutionary (especially neuroevolutionary) approaches.

Today’s episode does get fairly deep into the weeds of ML theory at points so may be best-suited to technical practitioners. That said, the broad strokes of the episode could be not only informative but, again, could indeed be life-perspective-altering for any curious listener.

In this episode, Ken details:
• What genetic ML algos are and how they work effectively in practice.
• How the Objective Paradox — that you fail to achieve an objective you seek — is common across ML and human pursuits.
• How an approach called Novelty Search can lead to superior outcomes than pursuing an explicit objective, again both for machines and humans.
• What Open-Ended A.I. is and its intimate relationship with AGI, a machine with the same learning potential as a human.
• His vision for how A.I. could transform life for humans in the coming decades.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.