My primary stream of research focuses on the sociotechnical dimensions of artificial intelligence/machine learning (AI/ML) systems and the communities, cultures and practices through which they are enacted. 

 

Some areas I have examined in this context include:

• Voice analytics technologies 

• Speech Emotion Recognition (SER) technologies 

• Voice "engines" (cloning / synthesis technologies)

• Speaker recognition / voice biometric systems 

• Acoustic Gunshot Detection Systems (AGDS)

• Benchmarking practices

• Venture capital narratives

 

In my current research, I'm invested in understanding the epistemic limits of machine learning to critically gauge what kinds of tasks ML should and shouldn't be used for. Here, generative AI and the cultures of benchmarking in ML are areas that I have started to explore alongside the epistemic foundations of psychometrics, specifically the "science" of intelligence.