David K.A. Mordecai, Samantha Kappagoda and John Y. Shin co-authored an article published in the Unintended Consequences (Fall 2022) Issue of the American Bar Association (ABA) SciTech Lawyer. The article is entitled Objects May be Closer than They Appear: Uncertainty and Reliability Implications of Computer Vision Depth Estimation for Vehicular Collision Avoidance and Navigation (Part 1 of 2).
Recent events accompanying increased adoption of machine learning applications of computer vision to safety-critical use-cases for cyberphysical systems has sharpened focus on the necessity of risk mitigation, reliability, safety, and security. An emergent risk domain across embedded cyberphysical systems involves the proliferation of camera-based autonomous driver assistance and vehicular navigation systems and the application of computer vision technology to perform the complex tasks of depth estimation, as well as object detection and image recognition. The first installment in this series will primarily focus on depth estimation tasks associated with operating and environmental conditions as well as spatial and temporal scales generally applicable to highway, rural and suburban settings.
ABA SciTech Lawyer endeavors to provide information related to current developments in law, science, medicine, and technology of professional interest to members of the ABA Section of Science & Technology Law.
Mordecai and Kappagoda are active members of the ABA Science and Technology (SciTech) Law Section. Ms. Kappagoda has been newly appointed as Vice-Chair of the Big Data Committee, and Vice-Chair of the Internet of Things Committee. She was reappointed, and continues to serve as Vice-Chair of the Insurance Technology and Risk Committee since 2019. Dr. Mordecai has been reappointed as Chair of the Space Law Committee, and Co-Chair of the Nanotechnology Committee, and previously also served as Vice-Chair of the Artificial Intelligence (AI) and Robotics from 2018 to 2022.
In addition, Dr. Mordecai has been an invited speaker at the AI & Robotics Institute in both 2021 and 2020. Both Kappagoda and Mordecai were invited speakers at the 2019 American Bar Association Annual Meeting and 34th Intellectual Property Law Conference (ABA-IPL) in Crystal City, Virginia. Dr. Mordecai previously authored the article Automated Personal Assistants with Multiple Principals: Whose Agent Is It? in the Winter 2020 edition of ABA SciTech Lawyer.
Dr. Mordecai is Co-Managing Member of Numerati Partners, Adjunct Professor of Econometrics and Statistics at the University of Chicago Booth School of Business, and Visiting Scholar at Courant Institute of Mathematical Sciences NYU, advising research at RiskEcon® Lab @ Courant Institute. Ms. Kappagoda is Co-Managing Member of Numerati Partners and Visiting Scholar at Courant Institute of Mathematical Sciences NYU, co-advising research at RiskEcon® Lab @ Courant Institute. Mr. Shin is Senior Research Associate (Enumeration Evaluation Lead) at Numerati Partners.
Numerati® Partners LLC coordinates a data analytics and technology development ecosystem, with the mission of advancing and fostering the next generation of scalable data-intensive risk and liability management enterprises. The firm provides resources fundamental to advancing the development of nascent leading-edge inferential surveillance, monitoring, and predictive analytics technologies for deployment within the RiskTech domain: risk technologies associated with adaptive distributed, networked and embedded systems such as remote sensing, agent-oriented data analytics, computing and control systems. Numerati® Partners curates integrated RiskTech solutions as well as forensic and use-case applications in RiskTech sub-domains such as LitTech, RegTech, FinTech and InsurTech (litigation technology, regulation technology, financial technology and insurance technology).About RiskEcon® Lab @ Courant Institute
The mission of RiskEcon® Lab for Decision Metrics @ Courant Institute of Mathematical Sciences NYU is the development of experimental testbeds and analytics that employ high-dimensional datasets from innovative sources by applying a range of computational and analytical methods to commercial and industrial sensor networks and edge-computing for distributed, embedded and autonomous systems, focusing primarily on research and development (R&D) of remote and compressed sensing, anomaly detection, forensic analytics and statistical process control. By employing applied computational statistics within the context of robust and scalable data analytic solutions, our goal is robust integration of machine learning with signal processing for measurement and control, in order to conduct research fundamental to large-scale, real-world questions in risk and liability management. RiskEcon® Lab enables, facilitates and coordinates academic research focusing on these patterns and trends, through the development of commercially-viable, analytic applications employing computational statistical tools in conjunction with innovative and non-traditional data structures. In addition, the Lab’s activities involve the advancement of applied mathematical statistics and computational economics, through interdisciplinary post-doctoral, postgraduate, graduate research and education in data science and social computing in the public interest.