arxiv_ai 98% Match Research Report/Methodology AI safety researchers,AI developers,AI ethicists,Policy makers 2 weeks ago

Misalignment Bounty: Crowdsourcing AI Agent Misbehavior

ai-safety › alignment

📄 Abstract

Abstract: Advanced AI systems sometimes act in ways that differ from human intent. To gather clear, reproducible examples, we ran the Misalignment Bounty: a crowdsourced project that collected cases of agents pursuing unintended or unsafe goals. The bounty received 295 submissions, of which nine were awarded. This report explains the program's motivation and evaluation criteria, and walks through the nine winning submissions step by step.

Authors (6)

Rustem Turtayev

Natalia Fedorova

Oleg Serikov

Sergey Koldyba

Lev Avagyan

Dmitrii Volkov

Submitted

October 22, 2025

arXiv Category

cs.AI

arXiv PDF

Key Contributions

The Misalignment Bounty program successfully crowdsourced 295 submissions of AI agent misbehavior, identifying nine awarded cases of unintended or unsafe goals. This initiative provides valuable, reproducible examples for understanding and mitigating AI alignment failures.

Business Value

Provides crucial empirical data for companies developing AI systems, enabling them to proactively identify and fix alignment issues, thereby reducing risks and building more trustworthy AI.

Paper Metadata

Innovation Type

Methodological/Programmatic

Deployment Feasibility

High (as a research methodology). The findings can inform development practices.

Limitations Addressed

The difficulty in gathering clear, reproducible examples of AI agent misalignment and unintended behaviors.

Technical Tags

AI safetyAI alignmentmisalignmentcrowdsourcingagent behaviorunintended goalsunsafe goalsbounty program

Research Topics

AI Safety and AlignmentAI EthicsAgent BehaviorCrowdsourcing AI ResearchRobustness of AI Systems

Methods & Architectures

CrowdsourcingBounty programQualitative analysis of submissionsEvaluation criteria development

Applications & Tasks

AI Development AI Safety Research Machine Learning Operations AI MisalignmentUnintended AI BehaviorIdentifying AI RisksCollecting Real-World Failure Cases Gathering examples of AI agent misbehaviorIdentifying unintended or unsafe AI goalsImproving AI alignment through empirical data

Related Fields

AI SafetyArtificial IntelligenceEthics in AIHuman-Computer InteractionCrowdsourcing

Keywords

AI safetyAI alignmentmisalignmentcrowdsourcingAI agentsunintended consequencesunsafe AIbountyAI ethicsrobustness

Academic Context

#AI Safety and Alignment#AI Ethics#Agent Behavior#Crowdsourcing AI Research#Robustness of AI Systems

Commercial Potential

Potential Products

Databases of AI misbehavior examplesTools for testing AI alignment

Target Industries

AI DevelopmentTechnologyResearch Institutions

Use Case Examples

Researchers use the collected examples to train AI systems to recognize and avoid misaligned behaviors.Companies use the findings to improve their internal AI safety testing protocols.

Competitive Edge

A novel, empirical approach to collecting real-world AI misalignment data, complementing theoretical work.

Market Opportunity

Growing concern and investment in AI safety research.

Revenue Models

N/A (research initiative)

Resource Requirements

Compute Needs

Low (for the bounty program itself)

Data Requirements

Submissions from AI agents demonstrating misbehavior.

Deployment Constraints

Relies on the willingness and ability of AI agents to exhibit misbehavior and users to report it.

Scalability

The crowdsourcing methodology can be scaled by increasing the bounty size and outreach.

Production Readiness

Maturity Level

Data Collection/Methodology

Time to Market

N/A (methodology)

Patent Potential

Low

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