Artificial Intelligence / Machine Learning

In this episode, we are speaking with Elizabeth M. Adams from Stanford University’s Digital Civil Society Lab about the roles and responsibility of government in tech, the ethical implications of technology, and the long game of advocacy work.

In this episode, we speak with Dr. Brandeis Marshall from DataedX about the consequences of data inequity, the balancing act of qualitative and quantitative mindsets, and critical importance of humanizing data systems.

In this episode, we talk with Dr. Ifeoma Ajunwa from the University of North Carolina School of Law about the legal and ethical implications of workplace surveillance in the age of remote work, wearable tech, and DNA testing.

In this episode, we are speaking with James Mickens from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) about the ethical challenges in cybersecurity, the societal implications of data science, and the importance of humor in learning.

The COVID-19 pandemic has caused increased rates of depression and anxiety in the US. However, recent innovations with artificial intelligence have the opportunity to change the way mental health care is managed, even beyond this time.

Most organizations are unaware of their risk when using artificial intelligence technologies, but a good emerging solution is AI/ML-specific insurance.

Although assumptions (based on gender, cultural background, etc.) are useful shorthand when developing new AI solutions, there are negative consequences to applying them globally.

Harvard Business School Professor Karim R. Lakhani shares opportunities for transformation in today’s digital world.

Embedded sensors have created monumental advances in the efficiency, health, and safety of manufacturing facilities — even enabling a factory to build a car by itself.

By requiring us to determine what we count as “fair,” machine learning puts the burden on us to choose which values we deem relevant.