Data Justice Lab uses AI to help state and local governments enforce laws and regulations by comparing what companies claim to do with what those laws require and how audit and compliance systems are intended to work. Our platform generates explainable flags tied to specific legal requirements, produces exportable evidence for investigators, and tracks remediation. We start with synthetic or redacted data, protect privacy, and integrate with existing workflows—no new legislation required. Agencies can pilot a narrow scope, measure accuracy and review-time savings, and scale to high-risk AI, consumer protection, labor, and civil-rights enforcement.
CEO
Michael Santoro is a Silicon Valley founder of three legal-AI companies and a professor who helps law firms and legal departments make AI legal-grade—auditable, confidential, supervised, and aligned with the Model Rules.
Co-Chief Technology Officer and Director of Government Relations
Dr. Shannon Harris earned a PhD in Business Analytics and Operations from the University of Pittsburgh in 2016.
Co-Chief Technology Officer
Dr. Michele Samorani is an Associate Professor in the Department of Information Systems & Analytics, and the Program Director of the Master of Science in Information Systems.
Michael Santoro is a Silicon Valley founder of three legal-AI companies and a professor who helps law firms and legal departments make AI legal-grade—auditable, confidential, supervised, and aligned with the Model Rules. His work emphasizes governance that works in practice: showing sources, maintaining matter-level logs, naming a responsible lawyer, requiring human sign-off for consequential steps, and testing for bias with clear remedy paths. The result is defensible work product, faster cycles, higher realization, and reduced rework.
At Santa Clara University, he teaches Management & Entrepreneurship, translating AI policy into usable playbooks for courts, regulators, firms, and general counsels. His current writing develops a practical “legal-grade” checklist for deploying AI without sacrificing judgment, confidentiality, or candor.
Beyond legal AI, Michael’s broader work spans business ethics, business & human rights, and bioethics. He is dedicated to transforming ethics into workable policy—equity-safe, transparent, and operational—particularly in health and life sciences, where privacy, provenance, and human oversight are non-negotiable.
He is also deeply committed to access to justice. Michael believes AI efficiency should expand, not limit, who can afford a lawyer, and he helps legal teams channel saved time into pro bono and lower-cost services.
Dr. Shannon Harris earned a PhD in Business Analytics and Operations from the University of Pittsburgh in 2016. Her research interests include mathematical and empirical modeling with a focus on healthcare applications. Primarily, she analyzes the attendance behavior of patients to outpatient clinic appointments, and how that behavior affects a clinic’s scheduling practices. Additionally, she has projects researching racial bias in healthcare scheduling, and how people-centric operations affect patients’ transition of care from the hospital to home. Her work has been published in the European Journal of Operational Research, Manufacturing and Service Operations Management, Journal of Operations Management, Military Medicine, and the Journal of Multi-Criteria Decision Analysis.
Shannon worked as a management consultant at Deloitte Consulting and as a cost analyst at Technomics, Inc. She has served as a track chair for several INFORMS and CORS conference sessions, and has served on the board of the INFORMS Minority Issues Forum (MIF), INFORMS Diversity Committee, and the PhD Project student planning committee.
Dr. Michele Samorani is an Associate Professor in the Department of Information Systems & Analytics, and the Program Director of the Master of Science in Information Systems.
His research combines machine learning and optimization techniques to build decision support systems that improve companies’ business processes and information flow. His areas of research include machine learning and racial disparity in medical scheduling, relational data mining, text mining, and metaheuristic optimization.
Several of his projects were carried out in collaboration with several organizations, such as a healthcare organization dedicated to Black women, a nonprofit mental health center, a community legal center, and a pharmaceutical company.
Michele’s research has been published in top outlets in Information Systems, Operations Management, and Operations Research, such as MIS Quarterly, Manufacturing & Service Operations Management, Production and Operations Management, INFORMS Journal on Computing, and Decision Support Systems, and was featured in top-tier media and included in a United Nations report.
