Human Dignity and Agency in the Workforce in the Age of Agentic AI
Supported by the ND-IBM Tech Ethics Lab, this project studies how workplace AI can preserve human dignity and agency and support accountable, human-centered decision-making.
Professor, Mendoza College of Business
University of Notre Dame
My current research focuses on redefining the Corporate Social Responsibility in the Big Data and AI era, involving topics such as AI governance, privacy protection, ethical data practices, analytical transparency and accountability, etc. Specifically, I am developing methods and techniques that support the goals of fairness and transparency through all stages of the data lifecycle and analytical activities: beginning with data collection and acquisition, through cleaning, integration, analysis and ultimately decision making.
I was a Professor of Management at the Warrington College of Business. I also directed the AI, Cybersecurity Governance and Privacy Management Center.
I directed the Kogod Cyber Governance Center (KCGC), while serving as a Professor of Information Technology and Analytics at the Kogod School of Business. Under my leadership, KCGC secured millions of dollars in research support from federal agencies such as the National Science Foundation and the Defense Advanced Research Project Agency, and generous donations and gifts from industry leaders such as Amazon, Discovery (now Warner Bros. Discovery), Facebook, Financial Industry Regulatory Authority, Marriott, Raytheon, etc.
As a tenure-track and then tenured professor at Penn State, I received the National Science Foundation’s CAREER award in 2010, and published over 100 research papers on information privacy and security, human-computer interaction, and data analytics. My work was recognized by more than 10 best paper awards and nominations at various leading conferences. I was also awarded the endowed PNC Technologies professorship in 2010.
I was a program director at the National Science Foundation for 3 years, during which I served on a broad spectrum of national leadership committees, including co-chairing the Federal Privacy R&D Inter-agency Working Group to implement the National Privacy Research Strategy in 2016.
Editor-in-Chief. As the only business-research-focused journal published by the Association for Computing Machinery (ACM), the world’s largest scientific and educational computing society, ACM Transactions on Management Information Systems publishes the highest quality papers about the design, development, assessment, and management of information technology and systems within organizations, businesses, and societies.
Member, Board on Human-Systems Integration (BOSHI). As part of the National Academies’ Division of Behavioral and Social Sciences and Education, BOHSI provides new perspectives on how to leverage human factors and human-centered design principles through a systems approach to enhance both individual and organizational performance.
Department Editor (Governance and Policy Department). IEEE Security & Privacy publishes “articles with both a practical and research bent by the top thinkers in the field of security and privacy, along with case studies, surveys, tutorials, columns, and in-depth interviews and podcasts for the information security industry”.
Associate Editor. In terms of methodology, my editorial interests include machine learning, analytical modeling, meta-analysis, survey, and experiments. I am particularly interested in topics related to the societal implications of machine learning and data analytics, such as fairness in machine learning, data ethics, data privacy, cybersecurity, social media, etc.
Senior Editor. The editorial objective of AIS Transactions on HCI is to “enhance and communicate knowledge about the interplay among humans, information, technologies, and tasks in order to guide the development and use of human-centered Information and Communication Technologies (ICT) and services for individuals, groups, organizations, and communities”.
Associate Editor. My editorial interests included both behavioral research and studies that collect and analyze “organic data”, which are generated organically without explicit research design, e.g., data originated from mobile applications, social interactional data from social networking sites, Twitter feeds, etc. I also had a strong interest in papers that extend our knowledge of methods by combining “designed data” and “organic data” in research design.
Associate Editor. IEEE Security & Privacy publishes “articles with both a practical and research bent by the top thinkers in the field of security and privacy, along with case studies, surveys, tutorials, columns, and in-depth interviews and podcasts for the information security industry”.
Committee Member. In Open Science by Design: Realizing a Vision for 21st Century Research, the committee provided guidance to the research enterprise and its stakeholders as they work to achieve open science. The committee offered 5 recommendations and several implementation actions, with a focus on solutions that move the research enterprise toward open science.
Co-Chair. The working group developed and then implemented the National Privacy Research Strategy, which established the objectives and priorities for Federally-funded privacy research, provided a framework for coordinating privacy research and development, and promoted multidisciplinary research that recognizes privacy needs of individuals and society and the responsibilities of the government.
See my CV for a list of completed research projects.
Supported by the ND-IBM Tech Ethics Lab, this project studies how workplace AI can preserve human dignity and agency and support accountable, human-centered decision-making.
Supported by the U.S. Army Research Institute (ARI), Basic Research Program, this project uses machine learning to study the direction and timing of causal relationships.
We thank Meta Research for the generous support of our project “Addressing biases in measurement of self-reported privacy constructs”.
See my Google Scholar profile for publications before 2019
This position paper argues that alignment research should probe reflexive fragility and the broader risks it reveals beyond scheming.
This paper examines why decoding can sharpen without resolving uncertainty through two regimes: lock-in and confusion.
In an article accepted for publication at MIS Quarterly, we examine why machine learning needs theoretical guidance to support inductive theory building.