Machine Learning, AI and Ethics Meetup

Short thesis

Hi! This is a meetup for everyone who works on the ethical dimensions of machine learning and artificial intelligence. Academics, journalists, regulators, policy makers, concerned data scientists, start up founders and activists - let's talk.



"What Are You Doing To My Data?

By developing models to guide law enforcement, models to predict recidivism, models to predict job performance based on Facebook profiles, data scientists are playing high stakes games with other people’s lives. Models make mistakes; a perfectly qualified and capable candidates may not get her dream job. Data is biased; word embeddings (mentioned above) encode the meaning of words through the context in which they are used, allow simple analogies, and, trained on Google News articles, reveal gender stereotypes—”man is to programmer as woman is to homemaker”. Faulty reward functions can cause agents to go haywire. Models are powerful tools. Caution.

In 2016, Cathy O’Neill published Weapons of Math Destruction on the potential harm of algorithms which got significant attention (e.g., Scientific American, NPR). FATML, a conference on Fairness, Accountability, and Transparency in Machine Learning had record attendance. The EU issued new regulation including “the right to be forgotten”, giving individuals control over their data, and restricts the use of automated, individual decision-making especially if decisions the algorithm makes cannot be explained to the individual. Problematically, automated, individual decision-making is what neural networks do and their inner workings are hard to explain. 

2017 will see companies grappling with the consequences of this “right to an explanation” which Oxford researchers have started to explore. In 2017, we may come to a refined understanding of what we mean when we say: “a model is interpretable”. Human decisions are interpretable in some sense, we can provide explanations for our decisions, but not others, we do not (yet) understand the brain dynamics underlying (complex) decisions. We will make progress on algorithms that help us understand model behavior and exercise the much needed caution when we build predictive models areas like healthcare, education, and law enforcement."