Every search, click, purchase, or pause on a video gets turned into a monetizable data point. Tech corporations use AI to collect, analyze, and sell these behaviors. The algorithms predict, influence, and monetize.
Compensating individuals for their data misses the deeper issue: human behavior has been turned into an economic asset. AI ethics, surveillance capitalism, and data privacy get discussed. But converting behavior itself into a tradeable good remains undertheorized.
This goes past ownership or privacy. AI systems manipulate and exploit human behavior for profit, often without people knowing.
These systems affect personal autonomy, power dynamics, and marginalized communities. They demand more rigorous investigation than what scholarship currently offers.
Where Current Research Stops Short
Privacy and data ownership dominate the conversation. Shoshana Zuboff's The Age of Surveillance Capitalism exposed the power asymmetries between corporations and individuals. But that focus neglects how AI treats behavior itself as a raw material. Behavior turned into data erodes autonomy — our actions get shaped by systems designed to make us engage, consume, and produce more data points.
Research rarely examines how AI manipulates behavior beyond privacy and fairness. Kate Crawford and Meredith Whittaker highlighted AI's social and environmental harms. Yet the conversation stops before asking how AI shapes human action for corporate alignment, at the cost of individual autonomy. Engagement-driven systems distort behavior for financial gain under the banner of "better user experiences."
The work stays siloed. Behavioral economists study incentives. Psychologists study cognitive effects of digital environments. Ethicists study consent. Rarely do these fields combine to show how AI turns behavior into revenue. This fragmentation limits what countermeasures we can build.
AI reinforces structural inequalities and extends capitalist exploitation into new digital territory. But research rarely examines this directly. Marginalized communities, already vulnerable to predatory data practices, get hit hardest. Ruha Benjamin and Safiya Umoja Noble exposed the racialized and gendered dimensions of algorithmic bias. But behavioral commodification among marginalized populations remains underexplored. This blind spot conceals how AI perpetuates exploitation under efficiency and personalization.
People Working on This
- Shoshana Zuboff (Harvard Business School) provided the foundation on surveillance capitalism. Her work is macroeconomic; it doesn't trace how AI systems influence and commodify individual behaviors.
- Kate Crawford (AI Now Institute) pushed for accountability and transparency around AI's environmental and social impacts. The micro-level mechanics of behavior commodification — how AI manipulates user actions for profit — aren't fully traced.
- MacArthur grantee Ruha Benjamin (Princeton) and Safiya Umoja Noble (UCLA) showed how algorithmic bias operates along racial and gender lines. Their work opens doors for understanding how marginalized communities are disproportionately targeted, but behavior itself as a tool of exploitation needs further study.
What's Needed
Addressing behavioral commodification means moving past existing frameworks. A few directions:
Cross-disciplinary work. Behavioral economics, AI ethics, psychology, sociology, and political economy need to talk to each other. Only combined analysis can show how AI manipulates behavior, and what the consequences are.
Autonomy and manipulation. Research has to examine how recommendation engines, predictive algorithms, and engagement-driven systems nudge users toward corporate-aligned actions, invisibly.
Power and exploitation. AI entrenches existing power structures. It's essential to trace how these systems commodify marginalized communities' behaviors for profit while offering nothing back.
Regulation beyond privacy. Frameworks must address behavioral commodification directly — transparency, algorithmic accountability, and protections for individual autonomy. Alternative AI development models that prioritize human dignity over short-term profit are worth exploring.
Turning behavior into a tradeable asset is one of the most ethically loaded issues of our time. Privacy and algorithmic bias are important fronts. But the deeper question — how AI shapes and monetizes human action, and what that means for autonomy, power, and equality — needs its own focus.
A multidisciplinary approach can shift how we understand and address these issues. Getting past surface-level discussions of privacy and data ownership opens room for frameworks that protect autonomy and resist exploitation.