The great power of AI: Algorithmic mirrors of individuals and society
Following the progress in computing and machine learning algorithms as
well as the emergence of big data, artificial intelligence (AI) has
become a reality impacting every fabric of our algorithmic society.
Despite the explosive growth of machine learning, the common
misconception that machines operate on zeros and ones, therefore they
should be objective, still holds. But then, why does Google Translate
convert these Turkish sentences with gender-neutral pronouns, “O bir
doktor. O bir hemşire”, to these English sentences, “He is a doctor.
She is a nurse”? As data-driven machine learning brings forth a
plethora of challenges, I analyze what could go wrong when algorithms
make decisions on behalf of individuals and society if they acquire
statistical knowledge of language from historical human data.
In this talk, I show how we can repurpose machine learning as a scientific tool to discover facts about artificial and natural intelligence, and assess social constructs. In the first part, I focus on individuals and demonstrate how machines that learn the unique linguistic style of an individual in a supervised learning setting can be privacy infringing. In the second part, I shift the focus on society and prove that machines trained on societal linguistic data inevitably inherit the biases of society. To do so, I derive a method that investigates the construct of language models trained on billions of sentences collected from the World Wide Web. I conclude the talk with future directions and open research questions in the field of ethics of machine learning.
Aylin Caliskan is a postdoctoral researcher and a fellow at Princeton University’s Center for Information Technology Policy. Her research interests include the emerging science of bias in machine learning and fairness, AI ethics, data privacy, and security. Her work aims to characterize and quantify aspects of artificial and natural intelligence using a multitude of machine learning and language processing techniques. In her recent publication in Science, she demonstrated how semantics derived from language corpora contain human-like biases. Prior to that, she developed novel privacy attacks to de-anonymize programmers using code stylometry. Her presentations on both de-anonymization and bias in machine learning are the recipients of best talk awards. Her work on semi-automated anonymization of writing style furthermore received the Privacy Enhancing Technologies Symposium Best Paper Award. Her research has received extensive press coverage across the globe, contributing to public awareness on risks of AI. Aylin holds a PhD in Computer Science from Drexel University and a Master of Science in Robotics from the University of Pennsylvania.