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.
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
Big Data, Artificial Intelligence, Machine Learning and Data Protection
The United Kingdom Information Commissioner's Office
This discussion paper looks at the implications of big data, artificial intelligence (AI) and machine learning for data protection, and explains the ICO’s views on these. It defines big data, AI and machine learning, and identifies the particular characteristics that differentiate them from more traditional forms of data processing. Realizing the benefits that can flow […]
Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI
S. Bird, S. Barocas, K. Crawford, F. Diaz, H. Wallach
This paper points out that while computer science has been performing large-scale experimentation for a long time, advances in artificial intelligence, novel autonomous systems for experimentation are raising complex, unanswered questions for the field. Some of these questions are computational, while others relate to the social and ethical implications of these systems. In this paper, […]
Averting Robot Eyes
M.E. Kaminski, M. Reuben, C. Grimm, W.D. Smart
The authors argue that home robots will inevitably cause privacy harms while acknowledging that robots can provide beneficial services — as long as consumers trust them. This paper evaluates potential technological solutions that could help home robots keep their promises, avert their “eyes”, and otherwise mitigate privacy harms. The goal of the study is to […]
Ethically Aligned Design
The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems
The IEEE Global Initiative provides the opportunity to bring together multiple voices in the Artificial Intelligence and Autonomous Systems communities to identify and find consensus on timely issues. This document’s purpose is to advance a public discussion of how these intelligent and autonomous technologies can be aligned to moral values and ethical principles that prioritize […]
Law and Regulation of Artificial Intelligence and Robots: Conceptual Framework and Normative Implications
N. Petit
In light of the many challenges that affect attempts to devise law and regulation in a context of technological inception, this paper seeks to offer a methodology geared to the specific fields of AIs and robots. This paper addresses the following normative question: should a social planner adopt specific rules and institutions for AIs and […]
Machine Learning: The Power and Promise of Computers That Learn by Example
The Royal Society
This report by The Royal Society provides an excellent overview of machine learning, it’s potential and impact on society. Through this initiative, they sought to investigate the potential of machine learning over the next 5-10 years, and the barriers to realizing that potential. In doing so, the project engaged with key audiences — in policy, […]
Rethinking the Fourth Amendment in the Age of Supercomputers, Artificial Intelligence, and Robots
M. Reid
This paper posits that it is not farfetched to think law enforcement’s use of cognitive computing will extend to using thinking, real-time robots in the field in the not-so-distant future. IBM’s Watson currently uses its artificial intelligence to suggest medical diagnoses and treatment in the healthcare industry and assists the finance industry in improving investment […]
Equality of Opportunity in Supervised Learning
M. Hardt, E. Price, N. Sobrero
The authors of this paper use a case study of FICO credit scores to illustrate their notion that classification accuracy in supervised learning depends only on the joint statistics of the predictor and the protected attribute but not the interpretation of the individual features of the data. The study looks at the inherent limits of […]