The Behavioral Data Science Podcast

The term "bias" means different things to people coming from different scientific backgrounds. In this episode, Jake and David discuss those different definitions of bias, how those definitions impact the conversation around bias in AI, and recommendations for mitigating "bias" in AI systems, regardless of the point of view from which you come.

In this episode, Jake and David discuss the unique ethical concerns raised by LLMs, generative AI, agentic systems, and multi-modal AI. For BACB Ethics CEUs, go to this site: https://www.bdspodcast.com/

In this episode, Jake and David discuss the ever-present possibility of bias in AI systems, and the resulting work and implications that has to mitigate AI bias when using LLM-driven products.

In this episode, Jake and David reflect on the many fantastic conversations of Season 4, and their thoughts on where things might go next.

In this episode, we have the distinct honor of being joined by Dr. Callie Plattner, Jenn Godwin, and Matt Filer to discuss their perspectives on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joined by Kristine Rodriguez and Mahin Para-Cremer to discuss their perspectives on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joined by Tim Yeager, Dr. Kerri Milyko, and Dr. Zach Morford to discuss their perspectives on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joined by Dr. Linda LeBlanc and Dr. Brent Kaplan to discuss their perspectives on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joined by Dr. Duff Lotfizadeh to discuss his perspective on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joined by Dr. Adam Hahs to discuss his perspective on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joinedby Dr. Amber Valentino to discuss her perspective on quality measurement and client outcomes in Applied Behavior Analysis.

In this episode, we have the distinct honor of being joined by Dr. Cody Morris and Dr. Bryant Silbaugh to discuss their perspective on quality measurement and client outcomes in Applied Behavior Analysis.

This is the third of the three-part introduction to thisseason of the Behavioral Data Science Podcast. During this season, Jake and I discuss the intersection of behavior science and data science as they relate to client outcomes in Applied Behavior Analysis service delivery. In this episode, we discuss the importance and challenges of contextualizing outcome measures based on the unique clinical...

This is the second of the three-part introduction to this season of the Behavioral Data Science Podcast. During this season, Jake and I discuss the intersection of behavior science and data science as they relate to client outcomes in Applied Behavior Analysis service delivery. In this episode, we discuss the history of outcome measurement in Applied Behavior Analysis, broadly, and the role the da...

This is the first of the three-part introduction to this season of the Behavioral Data Science Podcast. During this season, Jake and I discuss the intersection of behavior science and data science as they relate to client outcomes in Applied Behavior Analysis service delivery. In this episode, we discuss the history of quality measurement in healthcare, broadly.

In this episode, Jake and David discuss articles from the ⁠recent issue of the Journal of Applied Behavior Analysis . They also discuss how the underlying technologies and concepts might be extended via data science techniques.

In this episode, Jake and David discuss articles from the recent issue of Artificial Intelligence in Medicine . They also discuss how the underlying technologies and concepts might be translated to behavioral health services.

OpenAI and Anthropic have recently released products where users can connect health-related data and receive healthcare advice and guidance. Jake and David discuss these products, the potential benefits to ease of access to personalized healthcare-related information, and the potential harms from inaccurate (or dangerous) recommendations and data privacy concerns. At the end of the episode, they e...

Realistically, you can't build any model of behavior-environment relations if you can't (a) find the data you need and (b) integrate those data into a usable database. In this episode of The Behavioral Data Science Podcast, we discuss the many considerations and decisions one needs to make. And, we do so by discussing a seven-year-long project Jake has been working on to build a usable dat...

Crucial to any behavioral data science project is identifying either (a) what behavior you want to analyze and how you'll get the data; or (b) what data you can get and what behaviors those data allow you to analyze well. In this episode, we chat about these decisions in the context of the literally wild behavior of birds at backyard feeders. For the interested, here's a link to the backya...

In each episode of Season 3, we take a claim from a news story, paper, or hot topic, and walk through how a behavioral data scientist would think about it: clarify the question, identify the operant and respondent principles potentially at play, design the data pipelines, choose the models, and turn the resulting insights into data-based behavior-change tools. In this episode, we take on the claim...

As we close out Season 2 and our emphasis on LLMs, we had the distinct privilege of chatting with Dr. Elizabeth Garrison. She is one of the few people in the world with domain expertise spanning behavior analysis (BCBA) and artificial intelligence (PhD). In this episode, we reflect on the state of AI research and industry work pre-ChatGPT and post-ChatGPT release, the shift in academic AI research...

"Bubbles" are an economic phenomenon characterized by a rapid increase in asset prices that far exceed the asset's underlying fundamental value, driven by speculative buying and herd behavior rather than intrinsic worth. In this episode, Jake and David ask, "Are we in an AI bubble?". And, if so, what might this mean for both individuals and organizations as they navigate the current AI strategic l...

In this episode, Jake and David discuss the burgeoning area of research looking at how interacting with LLMs impacts our skills and abilities in good and bad ways. As with most things in life, the effects are not black-and-white. And, we discuss strategies and tactics we can all engage in to try to get the benefits without the drawbacks.

Conversations around AI ethics often focus on a suite of incredibly important topics such as data security and privacy, model bias, model transparency, and explainability. However, each time we use large AI models (e.g., diffusion models, LLMs), we reinforce a host of additional potentially unethical practices that are needed to build and maintain these systems. In this episode, Jake and David dis...