Unpacking “Ethical AI”
A curated reading list
On September 25, 2019, Data & Society Executive Director Janet Haven delivered the talk “Embrace Complexity: The New Rules of AI” at the Strata Business Summit. This reading list, prepared with Data & Society Researcher Emanuel Moss, provides links to research addressed in her talk and additional readings for anyone wishing to explore ethical AI more deeply.
As algorithmic- and data-driven technologies, often called artificial intelligence, take on an increasing role in our lives and in addressing some of the most vital problems of our time, people have raised real concerns about the ethical and moral implications of using these technologies. Critiques of AI have come from many corners; there are concerns about security and surveillance, racial and gender bias, accountability and responsibility, and the relationship between these tools and the ways of knowing the world that we all draw from.
Recently, the corporations and academic departments that develop AI technologies have begun to internalize some of these critiques and grapple with the challenges of building “ethical tech.” At the same time, governments are trying to regulate some of the technologies in meaningful ways. These approaches to governance have been both hyperlocal — Somerville, Massachusetts has banned facial recognition technology — and global — the UN’s Universal Declaration of Human Rights is providing guidance on how to govern the technologies they build. With all of these hands stirring the pot, “ethics” itself has become the grounds of contestation for who gets to shape the technologies that affect millions of people every day. What ethics is, where it can be found, and who gets a say in the matter are all open questions when it comes to the development and deployment of AI technologies.
This reading list is meant for anyone who wants to get a better sense of the landscape surrounding “ethical tech.”
What is clear is that any ethical AI worthy of the label is going to require an all-hands-on-deck approach. Companies, governments, academics, advocacy organizations, and individuals all have skin in the game and a role to play, particularly in elevating the voices of the least powerful, who are most likely to be exposed to technology-driven harms. As Virginia Eubanks warns in Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, “… while the most sweeping digital decision-making tools are tested in what could be called ‘low rights environments’ where there are few expectations of political accountability and transparency, systems first designed for the poor will eventually be used on everyone.”
This reading list is meant for anyone who wants to get a better sense of the landscape surrounding “ethical tech.” It features some of the more trenchant critiques of AI technologies and some early studies of the responses to those techniques. Hopefully, it can be a basis for understanding what some of the central concerns about AI technologies are, and how they’re being addressed.
Reading List
Embrace Complexity: The New Rules of AI
Works referenced in Janet Haven’s Strata Business Summit talk.
- Uberland: How Algorithms are Rewriting the Rules of Work , Data & Society Research Lead Alex Rosenblat, 2018.
- California Just Dropped a Bomb on the Gig Economy — What’s Next?, Andrew J. Hawkins, 2019.
- Machine Bias, Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner, 2016.
- Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System, 2016–17 Data & Society Lawyer-in-Residence Rebecca Wexler, 2018.
- Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, Virginia Eubanks, 2018.
- Gender Shades, Joy Buolamwini and Timnit Gebru, 2018.
- Safe Face Pledge, Algorithmic Justice League, 2018.
- Facial Recognition is the Plutonium of AI, Luke Stark, 2019.
- Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics, Data & Society Researchers Jacob Metcalf and Emanuel Moss and Founder and President danah boyd, 2019.
- Tutorial: 21 Definitions of Fairness and their Politics Arvind Narayanan, 2018.
Additional resources
Themes include ethics and AI, social subjectivities, and fairness, accountability, and transparency.
- Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction, Data & Society Research Lead Madeleine Claire Elish, 2019.
- Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning, Daniel Greene, Anne Lauren Hoffman, and Luke Stark, 2019.
- Toward an Ethics of Algorithms: Convening, Observation, Probability, and Timeliness, Mike Ananny, 2015.
- Artificial Intelligence: The Global Landscape of Ethics Guidelines, Anna Jobin, Marcello Ienca, Effy Vayena, 2019.
- Artificial Unintelligence: How Computers Misunderstand the World, Meredith Broussard, 2018.
- “Raw Data” Is an Oxymoron, edited by Lisa Gitelman, 2013.
- Data Feminism (esp. Chapter 2: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints), Catherine D’Ignazio and Lauren Klein, 2018.
- The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition, Os Keyes, 2018.
- Algorithms of Oppression: How Search Engines Reinforce Racism, Safiya Umoja Noble, 2018.
- The Seductive Diversion of “Solving” Bias in Artificial Intelligence, Julia Powles, 2018.
- Accountable Algorithms, Joshua A. Kroll, Joanna Huey, Data & Society Affiliate Solon Barocas, Edward W. Felten, Joel R. Reidenberg, David G. Robinson, and Harlan Yu, 2016.
- A Computer Program Used for Bail and Sentencing Decisions was Labeled Biased against Blacks. It’s Actually Not that Clear, Sam Corbett-Davies, Emma Pierson, Avi Feller, and Sharad Goel, 2016.
- On the (Im)possibility of Fairness, 2016–17 Data & Society Fellow Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian 2016.
- Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges, Corinne Cath, 2018.
- Big Data’s Disparate Impact, Data & Society Postdoctoral Scholar Andrew D. Selbst and Affiliate Solon Barocas, 2016.
- Technological Due Process, Danielle Keats Citron, 2015.
- How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms, Jenna Burrell, 2015.
- The Nice Thing about Context is that Everyone Has It, Nick Seaver, 2015.
Emanuel Moss is a researcher on the AI on the Ground initiative at Data & Society. Suggestions, comments, or noticed a resource we missed? E-mail the initiative: aigi at datasociety dot net.
