# CAREER: Inferring Specifications for AI by Modeling Humans

> **NSF 01003031DB NSF RESEARCH & RELATED ACTIVIT** · Brown University (RI) · $599,837

## Abstract

Artificial intelligence (AI) systems increasingly influence both high stakes and everyday decisions across many sectors of the economy. These systems, however, are not developed in isolation. Instead, they depend on people to provide instructions that describe what the system should do and what outcomes it should avoid. These instructions can take many forms. They may be written explicitly by domain experts or learned from data such as human preferences over outcomes. However, providing clear and reliable instructions for intelligent systems is difficult even for relatively narrow applications. Instructions can be too rigid, too vague, or simply incorrect, and any of these problems can cause systems to behave in unintended ways. These failures occur because instructions are created by people, and human reasoning is shaped by limited information, context, and common cognitive mistakes. As AI becomes more widespread, improving how systems interpret human intent will be essential for safety and reliability. This project addresses that challenge by studying how people communicate goals to machines and by designing AI systems that can interpret imperfect instructions by reasoning about the intent behind them. The expected outcomes include safer decision-making technologies and new tools that help organizations deploy AI more effectively.

This project develops computational foundations for learning AI specifications from imperfect human input. The research integrates reinforcement learning, Bayesian inference, and computational cognitive modeling with empirical studies of human decision making to better characterize how people communicate goals and where specification errors arise. The work is organized around three research thrusts. The first thrust, Modeling and Inferring AI Specifications, develops probabilistic models of human reasoning that capture systematic specification errors and uses these models to enable AI systems to infer more accurate goals from flawed i

## Key facts

- **NSF award ID:** 2541285
- **Awardee organization:** Brown University (RI)
- **SAM.gov UEI:** E3FDXZ6TBHW3
- **PI:** Serena Booth
- **Primary program:** 01003031DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev, ROBUST INTELLIGENCE, EXP PROG TO STIM COMP RES
- **Estimated total:** $599,837
- **Funds obligated:** $442,685
- **Transaction type:** Continuing Grant
- **Period:** 09/01/2026 → 08/31/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2541285

## Citation

> US National Science Foundation, Award 2541285, CAREER: Inferring Specifications for AI by Modeling Humans. Retrieved via AI Analytics 2026-07-03 from https://api.ai-analytics.org/grant/nsf/2541285. Licensed CC0.

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