ai/data ethics

I am the current director of the Center for AI and Data Ethics at University of San Francisco, where we work on creating educational resources and content for AI and data ethics issues, partner with organizations on practicum projects that have an ethics component, and offer ethics courses related to AI and data science.

Case Studies

In the past I’ve worked with undergraduate students on creating this set of case studies for any AI/Data Ethics course. They are suitable for both non-technical and technical audiences. The goal of the case studies project is to expand these case studies to include technical exercises for STEM students and discussion questions for law students.

Facial Recognition for Policing: Real-life Negative Consequences of Biased Algorithms
Themes: Algorithmic bias and fairness; privacy

Voice Assistants and Biometric Data: Amazon’s Violations of Children’s Privacy Rights
Themes: Privacy and data security

Racial Bias in Healthcare Data Solutions: Perpetuating Disparities in Medical Treatment Nationwide
Themes: Algorithmic bias and fairness; social impact

Failures of LLM-Generated Text Detection: False Positives Dilute the Efficacy of AI Detection
Themes: Academic integrity and truth

When Noone is Driving: Navigating Accountability via Cruise’s Driverless Vehicles
Themes: Transparency and explainable AI; automation; future of work

AI Art: Assistants, Replacements and Grifters
Themes: Future of work; copyright and intellectual property

Reading List

I also maintain this short reading list for our MSDS Ethics in Data Science course.