Jared Lee Katzman
I am a social-technologist, researcher, and advocate working at the intersection of economic democracy and digital innovation.
My work focuses on how we can design "anticipatory institutions"—governance models, alternative financing, and cooperative businesses—
that proactively protect workers and communities from the possible impacts of AI, before they occur.
My journey began as a software engineer in Big Tech.
While building machine learning systems at Amazon Web Services (AWS), I saw firsthand the limitations of the traditional approaches to "Responsible AI".
I realized that if we want to build technology that genuinely serves society, we cannot just audit algorithms after the damage is done.
We need to question why and how we build technology, and who we build it with.
I have now been pursuing my PhD at the University of Michigan School of Information (advised by Drs. Ben Green and
Tawanna Dillahunt),
where my doctoral research investigates how alternative economic models possess the structural power to hold technology accountable.
I study cooperative innovation networks across the globe—from the expansive Mondragon Cooperative in the Basque Country of Spain to emerging solidarity economies in the US—
to map how democratically governed networks successfully navigate the velocity of automation, scale cooperative tech innovation, and protect worker sovereignty.
To bridge this research with on-the-ground action, I lead participatory workshops that guide community members through speculative design frameworks that
create civically engaging spaces where people can voice their opinions, evaluate risks,
and ultimately turn their fears about technology into creative and imaginative strategies for building resilience in the present.
Today, I am translating this empirical research into practical interventions for the US and global solidarity economies. I work across three core pillars:
- Building & Advising: Collaborating with cooperative holding networks, alternative financial institutions, and social-innovation businesses to design tech infrastructure aligned with democratic values.
- Training: Developing incubators, educational frameworks, and mentorship programs for the next generation of technologists seeking to build outside the Silicon Valley paradigm.
- Advocacy & Speaking: Engaging with policymakers, labor organizers, and the public on protective policy frameworks, the limits of corporate AI ethics, and the future of economic democracy.
Experience
Center for Democracy and Technology
Conducted policy research on topics such as AI regulation and generative AI, auditing requirements of the Digital Services Act, and facial recognition uses by police. Work culminated in written blog posts and policy memos. ◦ Contributed to Requests for Comments for federal agencies like the National Telecommunications and Information Administration (NTIA) and prepped CDT executives for US Senate Congressional Hearings.
UM Science, Technology & Public Policy Program
Collaborated with community organization Detroit Disability Power to conduct technical landscape analyses on how automated hiring algorithms are impacting people with disabilities. Evaluated current regulations around algorithmic hiring and their gaps to assist with advocacy and education campaigns.
Microsoft Research
Led research for multiple projects, including statistical methods for polling and election forecasting, interactive data journalism to improve comprehension of politically-relevant data, and survey design for measuring impact of news media. Investigate a taxonomy for fairness and inclusiveness in image tagging and captioning AI services. Member of Fairness, Accountability, Transparency, and Ethics (FATE) reading group. Teaching Assistant for Microsoft Research Data Science Summer School, Summer 2021, an intensive, four-week hands-on introduction to data science for college students in the New York City area.
Out in Tech
Started Out in Tech U’s Digital Mentorship Program, where LGBTQ+ students work on projects with professional mentors over the course of 8 week semesters. Managed 4 volunteer coordinators to design and facilitate all digital programming for participants across the United States. Grew the program from an initial 30 participants to over 400 members annually. Participated in the Spring 2018 cohort as a mentor; Worked with a mentee, with no previous experience in data analysis, on how to use Python, Jupyter Notebooks, Pandas, NumPy, and Plotly to visualize the impact of America’s Opioid Crises.
Amazon Web Services
Founding engineer on team developing Machine Learning Bias and Explainability products and research across ML platform. Designed and implemented data explainability feature for SageMaker Autopilot within 1 month of joining team pre-launch. Researched technological and scientific vision and strategy for 5 new product proposals reviewed by senior leadership of AWS’s AI labs. Developed Entity Linking models on top of BERT which improved precision by 10% compared to baseline methods.
Researched, developed, and released IP Insights, a deep learning algorithm that learns the history of users' IP addresses to identify anomalous behavior such as fraudulent logins or account takeovers. Discovered and removed bottlenecks in algorithm's performance by tuning MXNet's distributed training, leading to a 50% reduction in training time for 75% of the comparable cost of scaling. Presented a talk on scalable, distributed machine learning on MXNet at Amazon's internal developer conference to 110 attendees. Designed and facilitated workshops on machine learning and security.
Bridgewater Associates
Built a real-time analytics platform with AWS services to transform security controls and operations from a perimeter, defensive model to a data-centric, automated-reasoning framework. Redesigned a batch processing architecture as a real-time, serverless, streaming framework which decreased a security control’s effect time from 24 hours to sub minutes and reduced monthly costs by a factor of 10.