Introduction
Modern AI simulates certain human capabilities, including reasoning, decision making, collaboration, and generativity. This skill is proving valuable in such contexts as medical diagnosis[i], business operations,[ii] and elder care.[iii] At the same time, adverse effects are also emerging with the use of AI, including errors, harmful behaviors, negative impacts on human cognition, labor market disruptions, homogeneity in AI-assisted writing, reduced diversity of perspectives,[iv] and other damaging phenomena.[v] Capabilities without a conscience, experience, or emotions engender both promise and peril.
Compounding the problem is ever-increasing AI quality, the emergence of AI agents with plan-do-check reasoning loops and resource access, and economic incentives for adoption. Leading AI companies now deliver AI coding experts, desktop agent assistants, and designers, inviting the offloading of even more work to AI. But what happens after humans cede control to AI? Can AI have agency, and what does that mean? Is AI to be trusted—will it do as we intend? What are the implications for labor markets, resource allocation, governance, and consumer protection? And how will AI influence and be influenced by culture? In this complex AI ecosystem, who is looking out for humans (and other living beings and the natural environment)?
Universities are a good candidate for this role. Scholars have recognized their unique place within the complex ecosystem and begun exploring the implications of the new AI expansion. Perhaps the most common approach is to investigate the impact on a particular academic field or domain following a formula of “AI for X,” such as AI for autonomous mathematics research,[vi] AI for political science research,[vii] or AI for teaching and learning.[viii] This approach is necessary to understand capability in, for example, the realm of cancer research.
We propose a less common strategy: “AI for Humanity.” We study how AI as a system can better advance humanity through understanding its system-level impacts on civil society.[ix] Possible goals for AI identified in this approach include disincentivizing harmful AI behaviors, preventing the erosion of human capacities, and mitigating legal, political, and social consequences of AI such as alienation. Our humanistic approach to AI advances ethical and helpful AI design through rigorous research to guide implementations of AI for Humanity.
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Conceptual Development
Meeting as an Advanced Study seminar on a monthly basis, a small interdisciplinary group of scholars (from Business, Computer Science, History, Information, Philosophy, Psychology, and Public Policy) explored and debated a wide-ranging set of topics within the theme of AI ethics and equity over the course of a year. The faculty refined and distilled their discussions into four key issues: agency, trust, political economy, and culture. The group divided itself into teams, and each assumed responsibility for a topic, led a seminar discussion based on common readings during the fall of 2024, and drafted an essay that drew on the readings and collective discussion for presentation to the group in the winter of 2025. In most cases, a graduate student collaborated with the teams to facilitate writing and development. In every case, essays were workshopped in the monthly seminars, facilitating a process of cross-disciplinary exchange and feedback that allowed the lead authors to integrate interdisciplinary perspectives from the collective.
Agency in Humans and AI explores how AI systems are increasingly capable of autonomous decision-making and task execution. As AI agents navigate real-world environments, their capacity to act independently raises fundamental questions about accountability, and about the purposes or values with which the agents are aligned: Whose purposes? Which values? With what effects? And with what forms of oversight and control? (See, for instance, the discussion of the MJ smear campaign, below.) These questions, in turn, raise issues tackled by the second essay, AI Trust and Trustworthiness. In order for human stakeholders to embrace AI agency responsibly, AI systems must, we argue, be transparent, reliable, and aligned with human expectations. What is the state of trust and how can we develop mechanisms to bridge the trust gap?
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The Trust Gap and What to Do About It
Shifting from agency to implications, the second essay entitled “AI Trust and Trustworthiness” [authored by John Carson, Dien Luong, and Colleen Seifert] explores whether it is possible to trust an AI system with varying degrees of agency.[xiii] The essay explores alternative interpretations about what it means to trust AI, who is responsible when trust is broken, and policy and legal mechanisms to address the trust gap, with the authors adopting a hopeful scepticism perspective.
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