The fast.ai ethics course

See ethics.fast.ai for the course

Data ethics covers an incredibly broad range of topics, many of which are urgent, making headlines daily, and causing harm to real people right now. A meta-analysis of over 100 syllabi on tech ethics, titled "What do we teach when we teach tech ethics?" found that there was huge variation in which topics are covered across tech ethics courses (law & policy, privacy & surveillance, philosophy, justice & human rights, environmental impact, civic responsibility, robots, disinformation, work & labor, design, cybersecurity, research ethics, and more-- far more than any one course could cover). These courses were taught by professors from a variety of fields. The area where there was more unity was in outcomes, with abilities to critique, spot issues, and make arguments being some of the most common desired outcomes for tech ethics course.

In this course, we will focus on topics that are both urgent and practical. In keeping with my teaching philosophy, we will begin with two active, real-world areas (disinformation and bias) to provide context and motivation, before stepping back in Lesson 3 to dig into foundations of data ethics and practical tools. From there we will move on to additional subject areas: privacy & surveillance, the role of the Silicon Valley ecosystem (including metrics, venture growth, & hypergrowth), and algorithmic colonialism. This course covers just a slice of what is a sprawling field, and I hope that it will be a helpful entry point for continued exploration.

This class was originally taught in-person at the University of San Francisco Data Institute in January-February 2020, for a diverse mix of working professionals from a range of backgrounds (as an evening certificate courses). There are no prerequisites for the course. This course is in no way intended to be exhaustive, but hopefully will provide useful context about how data misuse is impacting society, as well as practice in critical thinking skills and questions to ask.

Topics covered:

1. Disinformation
2. Bias & Fairness
3. Ethical Foundations & Practical Tools
4. Privacy & surveillance
5. Our Ecosystem: Metrics, Venture Capital, & Losing the Forest for the Trees
6. Algorithmic Colonialism, and Next Steps

Desired Learning Outcomes

1. Understand the impacts of data misuse, including unjust bias, surveillance, disinformation, and feedback loops. Understand the contributing factors to these impacts. Identify different types of bias.
2. Develop literacy in investigating how data and data-powered algorithms shape, constrain, and manipulate our commercial, civic, and personal experiences.
3. Analyze new scenarios and potential products to try to identify and mitigate potential risks.
4. Have a toolkit of ethical techniques and practices to implement in their workplaces