Announcing the Winners of BKC’s Inaugural Tech Policy Press Student Essay Contest
Agency, Trust, and Governance in the Age of AI
The Berkman Klein Center for Internet & Society and Tech Policy Press are pleased to announce the winners of BKC’s inaugural student essay contest.
The contest, launched during the Spring 2026 semester, called on Harvard students from across the University to share their perspectives on artificial intelligence and its implications for democracy and power, culture and creativity, and the future of work and learning. Selected essays published in Tech Policy Press (TPP) explore a range of issues, including the intellectual costs of offloading skills to AI, how the technology amplifies existing harms like racism and inequality, and how AI is impacting urban planning. Submissions were read by a selection of BKC staff, fellows, and community members, and winners were selected by TPP based on their unique approaches to the contest’s main topics of interest as part of a series titled "Agency, Trust, and Governance in the Age of AI."
“The selection committee was blown away by the topical breadth, insightfulness, and quality of writing submitted in this inaugural essay contest—it was very hard to select just a few essays,” said Executive Director Alex Pascal. “We are so grateful to Executive Editor Justin Hendrix and Tech Policy Press for collaborating with BKC to surface and platform these thoughtful, incisive emerging voices.”
The eight winning essays will be posted as available below.
Participants:
Isabel Adler (Harvard Graduate School of Design)
“AI May Dramatically Disrupt The American City. Are Urban Planners Ready?”
The emergence of AI threatens to exacerbate urban inequality. As AI reshapes cities, urban planners must move beyond pragmatic complacency and instead fight for cities that serve people over profit.
Amaia Aguilar (Harvard Business School)
“Addressing Regulatory Arbitrage in the AI Supply Chain”
This essay argues that AI repackages familiar harms, including bias, labor exploitation, and environmental racism, through regulatory gray areas, and that closing those gaps requires reclassifying them under existing law rather than just inventing new frameworks. 
“AI Didn’t Kill Design—It Exposed It”
As the product design profession grapples with the effects of AI on our work and the people we design for, this essay argues that AI itself is not the problem. Rather, AI has exposed the longstanding gap between the human-centered ideals of design and the reality of a profession whose decisions are often shaped by business incentives and organizational power.
“Whose Red Lines?”
This essay argues that Anthropic's contractual restrictions on DoD use of its models, while substantively correct, are undemocratic and brittle substitutes for legislative action on AI and civil liberties.
“When AI Agents Fail, People Ask the Wrong Question About Why”
This essay explores the gap between operation-level oversight and outcome-level accountability in AI agent systems. It argues that meaningful governance must ask not only which actions were approved, but whether anyone actually authorized the final outcome.
Hannah Kim (Harvard College)
"For Students, the Process of 'Becoming' is the Challenge No Chatbot Can Solve”
This essay is a personal reflection providing a small glimpse into the effects of generative AI in the classroom. It aims to simultaneously recognize the student struggle and encourage them to choose not the shortcut, but the longcut: human agency, independent thinking, and most of all, the joy of learning.
Evan Liu (Harvard School of Engineering and Applied Science)
"AI's Catastrophic Risk Isn't Rogue Machines, It's Cognitive Surrender”
The essay explores the long-term intellectual costs of AI as the ease of the technology encourages young people to take the easy way out as they doubt traditional paths, limiting their abilities to develop long-term skills that require steady investment and effort.
Ikenna Ogbogu (Harvard College)
“AI, Privacy, and the Hidden Architecture of Harm from Inference”
LLMs can infer sensitive personal information from seemingly mundane user data at a scale and precision that outpaces current data-centric privacy frameworks. A shift towards capability-rooted governance is necessary to modernize digital privacy legislation for the AI era.