Issue 2: Artificial Intelligence and Machine Learning: The Privacy Challenge

Notes from FPF

Building on our first issue, which discussed the various privacy challenges related to algorithmic accountability, Future of Privacy Forum’s Privacy Scholarship Reporter now turns its focus to thoughtful, academic considerations of the privacy challenges, and ethical data use considerations, of AI and Machine Learning.

Robot Hands

Artificial Intelligence is perhaps easier to intuitively grasp than to explicitly define – a truth that embodies the very challenge of trying to design machines that reflect what it’s like to be human. With every new technology, there is the question, “what ‘new’ privacy challenge does this platform, service, or capability pose?” Are there new privacy challenges in AI? Or perhaps there are just the same questions about consent, transparency, use, and control, but in new contexts and products? If there are new aspects – can the existing policy framework address them sufficiently? In AI, we may find that there are indeed challenges that expand beyond simply greater scope and scale, and that push us to define new tools with which to address them.

What we do know is that we cannot leave AI or Machine Learning in a black box. While retail recommendations for “people who bought this also bought that” seem clear and reasonable, what do we understand about stock-picking models that underlie our economy? A language translation program may feel straightforward, but what about the selection of news or travel options or job offers that are tied to your multi-language capabilities, or one’s demonstrated interest in – or distaste for – other cultures?

Machines are learning to read our emotions, interpret body language, and predict our comfort-seeking behavior. Are we building bigger and more impenetrable bubbles that will limit or divide us? Or are we creating more extended complex worlds that allow us to know and understand more about the world around us? How can we tell, and how can we understand and control our own data “selves” in the process? These are areas that deserve focused attention, and the scholarship addressing them is only just beginning.

In this issue are articles that provide an excellent basis and introduction to Machine Learning and Artificial Intelligence. They include publications that: propose methods that might combat potential discrimination and bias in predictive modeling based on AI; question whether existing regulatory and legal norms are sufficient for the challenges of AI (including privacy) or whether new frameworks may be desirable; ask how AI will impact individual rights, including 4th Amendment questions; delve into the tricky questions of ethics regarding the widespread use of AI systems in the general population; and explore what the presence of robots in our homes will do to our understanding of privacy.

Are there important scholarship missing from our list? Send your comments or feedback to fpf@fpf.org. We look forward to hearing from you.

Brenda Leong, Senior Counsel and Director of Strategy, FPF