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Human Impact


Research Mission

To develop equitable and trustworthy technology, we must understand how AI performs in practice, and guide and shape the way AI interacts with humans, their vital social structures and institutions, and the international order.

Artificial intelligence and machine learning are poorly understood, even within academic and research communities. The media portray a world of robots run amok; new applications and milestones are often described as “machines beating humans;” and influential public figures warn of job losses and more dire consequences. While some concerns are legitimate, misleading narratives too often distract from the pressing issues society is likely to confront as AI systems become commonplace.

Scholarly research is needed to measure and manage a host of critical issues, including the extent to which algorithms introduce, compound, or mitigate business risk or bias; a “responsibility gap” between decisions made by machines and people; the use of AI for surveillance, population control, and waging war; and the impact of AI on industry structure, labor markets, economic growth, and trade across nations. This research will inform engagement with industry, government, and civil society to beneficially guide AI’s development.

Recent News & Insights

By Graham Button
September 30, 2019
A key question before Stanford HAI and the California Future of Work Commission: Can AI be a positive, equalizing force or will it exacerbate inequalities?
September 2, 2019
The Non-Resident Fellowship supports social sector leaders to dedicate some of their time working on ideas that apply to broad swaths of civil society.


Sample Research Projects

Correcting Gender and Ethnic Biases in AI Algorithms

James ZouLonda SchiebingerSerena Yeung and Carlos Bustamante

Machine learning algorithms can contain gender and ethnic biases. As AI becomes ubiquitous, such bias if uncorrected can lead to inequities in service and discrimination against specific populations. In this project, we will develop a AI auditing where we leverage machine learning to discover and correct its own biases. Our goal is to make AI audit an integral component of machine learning in industry and academia.


The Impact of Artificial Intelligence on Perceptions of Humanhood

Benoît Monin and Erik Santoro

Can logic remain at the core of what it means to be human if AI clearly surpasses humans at it? Will society redefine what is core to the human experience as humans lose ground to AI on cognitive abilities that traditionally enshrined humans at the top of the animal kingdom? Drawing on social psychological theory and using randomized control trial (RCT) experiments, we seek to understand and forecast how the increasing presence of AI in daily life will change perceptions of what it means to be human.