Engineering an Ethical AI System
YOW! Data 2019
To improve people’s well-being, we must improve the decisions made about them. Consequential decisions are increasingly being made by AI, like selecting who to recruit, who receives a home-loan or credit card, and how much someone pays for goods or services. AI systems have the potential to to make these decisions more accurately and at a far greater scale than humans. However, if AI decision-making is improperly designed it runs the risk of doing unintentional harm, especially to already disadvantaged members of society. Only by building AI systems that accurately estimate the real impact of possible outcomes on a variety of ethically relevant measures, rather than just accuracy or profit, can we ensure this powerful technology improves the lives of everyone.
This talk focuses on the anatomy of these ethically-aware decision-making systems, and some design principles to help the data scientists, engineers and decision-makers collaborating to build them. We motivate the discussion with a high-level simulation of the "selection" problem where individuals are targeted, based on relevant features, for an opportunity or an intervention. We detail the necessary considerations and the potential pitfalls when engineering an ethically-aware automated solution, from initial conception through to causal analysis, deployment and on-going monitoring.
Simon T. O'Callaghan
Sr. Research Engineer
Data61 & Gradient Institute
I try to make artificial intelligence systems behave ethically. I've a background in machine learning and automated systems.
Sr. Research Engineer
My goal is to apply problem solving skills to technical problems with real impact, applying my skill set that bridges machine learning and engineering. My research interests include probabilistic data driven models, active learning, and overcoming data bias. I apply machine learning to real problems such as geo-spatial mapping, demographic modeling, and prediction of physical systems by consulting, designing statistical machine learning models and developing software solutions. I regularly communicate technical concepts to both academic and non academic audiences, and have also published scientific papers and open source software in multiple disciplines including machine learning, robotics, and geo-sciences.
I am a research scientist at the Gradient Institute working on developing ethical AI. I completed my PhD in Computer Science, focusing on causal inference in machine learning in 2018.