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Getting AIs working toward human goals study shows how to measure misalignment

April 14, 2025•Aidan Kierans•Original Link•2 minutes
AI Alignment
Human Values
Machine Learning

Aligning AIs with peoples goals and values is tricky A new technique quantifies how far off human and machine are from each other

Key Findings

  • Researchers developed a quantifiable method to measure alignment between human and AI goals
  • Misalignment peaks when goals are evenly distributed among agents
  • Same AI can be aligned in one context but misaligned in another

Why It Matters

  • Current AI safety research treats alignment as binary - new framework shows it's context-dependent
  • Helps developers move beyond vague goals like "align with human values" to specific contexts
  • Policymakers can use this to create standards for AI alignment

Research Methodology

  • Based on three factors:
    • Humans and AI agents involved
    • Their specific goals
    • Importance of each issue
  • Human value data collected through surveys, but AI goals remain hard to determine

Current Challenges

  • Today's black box AI systems (like LLMs) make goal interpretation difficult
  • Two potential solutions:
    • Interpretability research to reveal model "thoughts"
    • Designing transparent AI systems from the ground up

Future Directions

  • Researchers working on aligning AI to moral philosophy experts
  • Goal is to develop practical tools for measuring alignment across diverse populations

Example Case

  • AI recommender systems might align with retailer goals (increasing sales) but misalign with consumer goals (budgeting)

Related Resources

  • Alignment Survey
  • AI Standards and Regulations
  • YouTube: Recommender Systems

The study highlights the complexity of AI alignment and provides a framework for more precise measurement in real-world applications.

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August 27, 2025•Paolo Perrone

Beginner Guide to Building AI Agents with GPT and CrewAI

Learn how to create practical AI agents from scratch using GPT, n8n, CrewAI, and Streamlit with step-by-step instructions to ship your first agent in a weekend.

AI Agents
Machine Learning
CrewAI
August 21, 2025•Quantum News

New PING Method Enhances AI Safety by Reducing Harmful Agent Behavior

Researchers developed Prefix INjection Guard (PING) to mitigate unintended harmful behaviors in AI agents fine-tuned for complex tasks, improving safety without compromising performance.

AI Safety
Large Language Models
Machine Learning

About the Author

Dr. Lisa Kim

Dr. Lisa Kim

AI Ethics Researcher

Leading expert in AI ethics and responsible AI development with 13 years of research experience. Former member of Microsoft AI Ethics Committee, now provides consulting for multiple international AI governance organizations. Regularly contributes AI ethics articles to top-tier journals like Nature and Science.

Expertise

AI Ethics
Algorithmic Fairness
AI Governance
Responsible AI
Experience
13 years
Publications
95+
Credentials
2
LinkedInResearchGate

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