Building Human Capability in Algorithmic Environments
EVIDENCE | LITERACY | TOOLS | POLICY
ABOUT US
We dare to invent the future where everyone has meaningful agency to understand, navigate and shape the algorithmic systems that influence everyday life.
Algorithms increasingly shape everyday life. People need capability and agency, not merely protection from algorithms. The Internet User Behavior Lab (IUBL) builds human capability for an algorithmic world. Funded by the Internet Society, we develop research, tools and trusted infrastructure that help people understand, navigate and shape the systems that increasingly influence everyday life.
We are establishing Algorithmic Literacy as a capability for human agency — measuring whether it can be developed through targeted interventions, and testing whether increased literacy changes behaviour and agency. Building on this foundation, we are developing trusted infrastructure that enables people to provide meaningful feedback into algorithmic systems.
APPROACH
01
Conceptually Grounded
We apply Nobel Laureate Amartya Sen’s Capability Approach to algorithmic environments, asking what capabilities and freedoms people need to exercise meaningful agency within the systems that increasingly shape everyday life. The gives us an intellectual foundation.
02
Empirically Tested
Establishing Algorithmic Literacy as a measurable and developable capability requires us to test whether targeted interventions can strengthen it and whether increased literacy changes behaviour and agency.
03
Designed for Impact
Our vision of a future where everyone has meaningful agency to understand, navigate and shape algorithmic systems cannot be achieved alone. We work with funders, academics, policymakers, industry and, critically, civil society to turn evidence into tools and interventions that contribute to the trusted infrastructure needed for meaningful participation in algorithmic systems.
RESEARCH DIRECTIONS
01
Define & Measure
Our founding research proposed Algorithmic Literacy as a measurable capability for human agency. The 4R framework — Recognise, Reason, Respond and Retain — provides the foundation for defining and validating the construct.
02
Develop & Test
We work with behavioural scientists, computational scientists and trial designers to rigorously investigate whether Algorithmic Literacy can be strengthened through targeted interventions, and which approaches most effectively improve people's ability to understand and act within algorithmic environments.
03
Demonstrate Change
Critical to our thesis: does increasing it actually matter? We test whether increased Algorithmic Literacy translates into changes in online behaviour, human agency and information-seeking — and ultimately whether it contributes to healthier individuals and communities. If it does, we will have established a critical component of the trusted infrastructure needed for meaningful participation in algorithmic systems.
BLOG POSTS
Announcing the our first IUBL Fellows
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TEAM
Founded in Kansas City. Global in ambition.
Bryan C. Boots PhD
CO-FOUNDER &
DIRECTOR OF TECHNICAL RESEARCH
Bryan leads IUBL’s technical research, computational development and research publications. Associate Teaching Professor at UMKC and an expert in applied network science and computational social science. He holds a PhD in Systems Engineering from Colorado State University.
Alex Krause Matlack DBA
CO-FOUNDER &
DIRECTOR OF BEHAVIORAL RESEARCH
Alex leads IUBL’s behavioural research, research governance and public engagement. Assistant Teaching Professor at UMKC . Previously of Techstars and the Kauffman Foundation. Alex holds a DBA from University of Missouri-Saint Louis in Oranizational Behavior
Theo Richardson-Gool
CO-FOUNDER &
DIRECTOR OF STRATEGY
Theo leads IUBL’s strategic direction, policy engagement and research impact. He is a PhD by publication student in International Relations and Head of South East Asian Relations at the University of Bristol. Previously founder and CEO of Public Health Pathways.
FELLOWS
Prof. Paul Squires
Paul provides guidance on study design, methodology, publication strategy. Clinical Professor of Psychology at New York University. His research focuses on measurement and assessment, with current work in machine learning and natural language processing.
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FUNDED BY
