Authors

  • Chris Dede
  • Ashley Etemadi
  • Tessa Forshaw

Overview

This brief explores the concept of intelligence augmentation, which focuses on enhancing human judgment skills to work effectively alongside artificial intelligence (AI). It argues that rather than replacing human jobs, AI will change the nature of work, necessitating a shift in workforce development towards uniquely human skills.

Key Points

1. Concept of Intelligence Augmentation

  • Definition: A partnership between AI and humans where both contribute to better decision-making outcomes.
  • Example: Captain Picard and Data from Star Trek illustrate complementary skills—human judgment versus AI’s computational abilities.

2. Executive Summary

  • The future workforce will require a shift toward developing human judgment skills as AI excels in calculation and prediction.
  • Framing Questions:
    • What does intelligence augmentation look like?
    • Why can’t AI perform human-level judgment?
    • What judgment skills should workforce development prioritize?

3. Understanding Intelligence

  • Intelligence is defined broadly as the ability to reason, plan, and solve problems.
  • Roles:
    • Reckoning: AI’s strength in computation.
    • Judgment: Human ability to make ethical and context-sensitive decisions.

4. Practical Applications

  • Case Studies: Hypothetical examples of small business owners (Mel and Lisa) illustrate how AI can take over reckoning tasks, allowing humans to focus on judgment.
  • Future Skills: Workers must develop skills that leverage human judgment in contexts where AI cannot excel.

5. Limitations of AI

  • AI cannot replicate human judgment due to:
    • Embodied Experiential Knowing (EEK): Human knowledge shaped by physical experiences.
    • Collective Cultural Knowing (CCK): Understanding social cues and cultural contexts.
    • Personal Performative Knowing (PPK): Moral and ethical dimensions of human decision-making.

6. Workforce Development Priorities

  • Focus on enhancing EEK, CCK, and PPK through training that emphasizes practical, context-specific judgment skills.
  • Levels of Judgment: Micro (individual crises), meso (social-emotional issues), and macro (broader societal impacts).

7. Learning Models

  • Effective judgment skills can be taught through:
    • Immersive learning experiences.
    • Curriculum that integrates ethical considerations and emotional intelligence.

8. Next Steps for Research

  • Investigate the effectiveness of immersive learning for judgment skills.
  • Explore the balance between reckoning and judgment skills in workforce training.
  • Address algorithmic bias in AI and its implications for human judgment.

Conclusion

The brief emphasizes the need for workforce development programs to adapt to the changing landscape of work influenced by AI. By prioritizing the cultivation of human judgment skills, organizations can ensure that workers are equipped to thrive in a future where AI plays a significant role in decision-making processes.

Acknowledgments

The research was conducted by the Next Level Lab at Harvard Graduate School of Education, with funding from Accenture Corporate Giving.