Three Principles for the Responsible Use of AI
Cutting edge machine learning and AI technologies make increasingly consequential decisions, leading regulators, journalists, and the public to demand that they be built responsibly and ethically while protecting security and privacy. AI technologies undercut traditional data governance and compliance approaches, even as businesses that field data driven systems of all sorts - from the simplest descriptive analytics to the most sophisticated deep learning models - face a thicket of requirements and stakeholder demands. This workshop presents three actionable principles for building AI responsibly. These principles provide practical approaches to building AI systems that embody the right values, such as responsibility, fairness, ethics, and privacy, while still meeting their business goals and product requirements.
Joshua A. Kroll, PhD, is a computer scientist and leading expert recognized internationally for his work on responsibility and accountability in computer systems, especially automated decision-making systems and systems that use data science, machine learning, and artificial intelligence. As a Postdoctoral Research Scholar at the School of Information at the University of California, Berkeley, he studies how technology fits within a human-driven, normative context and how it satisfies goals driven by ideals such as fairness, accountability, transparency, security, privacy, and ethics.
Joshua has helped to develop and lead the field of fairness, accountability, and transparency in computer systems. His paper "Accountable Algorithms", published in the University of Pennsylvania Law Review (Vol. 165, 2016-17) received the Future of Privacy Forum's Privacy Papers for Policymakers Award in 2017. Joshua has also organized related research venues, including the Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and the Conference on Fairness, Accountability, and Transparency (FAT*).
Joshua holds a Bachelor's degree in physics and mathematics from Harvard College as well as a Master's degree and a Doctorate in computer science from Princeton University. His previous research work spans accountable algorithms, cryptography, software security, formal methods, Bitcoin, and several aspects of technology policy.