Research Ethics Reimagined Episode 2 Discussion Guide: “Research and AI with Mary L. Gray, PhD” 

In this episode of PRIM&R’s podcast, “Research Ethics Reimagined,” we explore the intersection of artificial intelligence, ethics, and studying human behavior in the digital age. Our guest is Mary L. Gray, PhD, Senior Principal Researcher at Microsoft Research and Faculty Associate at Harvard University’s Berkman Klein Center for Internet and Society. (Transcript available) 

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Discussion Questions 

1. The Evolution of Research Ethics 

  • Mary discusses how evolving research methodologies, studying online communities, challenged traditional ethical frameworks early in her career. What other novel approaches are emerging that may require rethinking ethical guidelines? 
  • She emphasizes the need for ethics to be viewed as a “methodological challenge” by all scientific disciplines. How can embedding ethical reasoning into the research design process become more widespread? 


2. Engaging with AI Disciplines 

  • Mary advocates for a “layered peer-review model” where ethical discussions happen alongside technical reviews within research teams before formal oversight. How could this approach work in practice? 
  • She stresses the importance of researchers critically examining assumptions baked into AI models and datasets. What strategies can oversight bodies employ to ensure this self-reflection occurs? 


3. Navigating the AI-Powered World 

  • Mary provides examples of how AI is already integrated into everyday products and services, often without users realizing it. How can the public be better informed about the prevalence and limitations of AI systems? 
  • She highlights areas where algorithmic decision-making may be inappropriate or flawed, like nuanced social interactions. How can ethical guidelines determine appropriate boundaries for AI deployment? 

Key Terms and Acronyms 

  • Algorithmic Bias: Systematic errors in artificial intelligence systems that lead to unfair outcomes, often due to flaws in the training data or modeling techniques.  

  • Computational Modeling: Using computers to simulate and study the behavior of complex systems by building mathematical models and analyzing data. 

  • Data Science: Extracting insights and knowledge from structured and unstructured data. 

  • Computer Vision: AI systems trained to identify and process digital images/videos like objects, people, text, etc. 

Additional Resources 

Ghost Work – Book by Mary L. Gray 

Mary L. Gray’s Website 

Microsoft Research Ethics Review Program