Davide Dell’Anna on hybrid intelligence, guidelines for human-AI teams, calibrating trust, and team ethics (AC Ep33)

2026-02-25 · Source: Humans + AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Governance · Depth: Intermediate, extended

Summary

Davide Dell’Anna, Assistant Professor of Responsible AI at Utrecht University, defines "hybrid intelligence" as a paradigm where human-AI teams achieve superior outcomes compared to either operating in isolation. This approach emphasizes augmenting human capabilities rather than replacing them, shifting the focus from "what can AI do to replace me?" to "how can we design the best possible team?" Dell'Anna highlights that current augmentation efforts often adopt a technology-centric view, overlooking the unique strengths and weaknesses of both human and AI team members. He draws parallels with human-animal teams, like shepherds and shepherd dogs, to illustrate effective inter-species collaboration. Key differences in human-AI teams include accountability, replaceability, and identity. Evaluating hybrid teams extends beyond task success to include member satisfaction, trust, and adaptability over time, necessitating a process-oriented assessment. The concept of "justifiability" is introduced, moving beyond mere explainability to align AI actions with shared team norms and values.

Key takeaway

For AI Product Managers designing collaborative systems, prioritize a "hybrid intelligence" framework that integrates human and AI strengths, rather than focusing solely on AI capabilities. Your design should account for human contextual knowledge and ethical framing, while AI handles data retrieval and processing. Emphasize justifiability over explainability, ensuring AI actions align with shared team values and norms to build appropriate trust and foster effective, evolving human-AI teams.

Key insights

Hybrid intelligence focuses on human-AI collaboration to achieve superior outcomes by leveraging complementary strengths and compensating weaknesses.

Principles

• Augment human capabilities, do not replace them.
• Acknowledge and leverage human and AI differences.
• Appropriate trust and shared ethics are crucial for team cohesion.

Method

Design human-AI teams by structuring roles and responsibilities, fostering shared awareness, breaking down tasks collaboratively, and ensuring continuous evolution with regular output reassessment.

In practice

• Consider human-animal teams as models for human-AI interaction.
• Implement process-oriented evaluation for hybrid team effectiveness.
• Develop new roles like "team designer" or "facilitator" for hybrid teams.

Topics

• Hybrid Intelligence
• Human-AI Collaboration
• AI Teaming Ethics
• AI Justifiability
• AI Team Design

Best for: AI Researcher, AI Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Humans + AI.