📖 Book Club
Intermediate
~2h

New Laws of Robotics

Defending Human Expertise in the Age of AI

Master Frank Pasquale's four laws for AI governance. Learn Intelligence Augmentation vs. replacement, distinguish high-stakes from low-stakes systems, and preserve meaningful work in the AI age. For a practical evaluation framework, see our AI Critical Thinking course.

By Frank Pasquale • Cornell Law • 2020 • PROSE Awards Finalist

TL;DR:

Pasquale's four laws for AI governance: complementarity (augment, don't replace), authenticity (don't counterfeit humanity), cooperation (avoid zero-sum arms races), and attribution (traceability for accountability)—preserve human expertise and meaningful work.

New Laws of Robotics — 4 laws: Complementarity, Authenticity, Cooperation, Attribution — IA vs AI

The Four Laws of Robotics

Unlike Asimov's laws (protecting humans from robots), Pasquale's laws govern how humans should develop and deploy AI.

1

Complementarity

Complementarity

AI should augment professionals, not replace them

Example: AI helps radiologists diagnose better, doesn't replace them

2

Authenticity

Authenticity

AI should not counterfeit humanity

Example: Chatbots must identify as AI, not pretend to be human

3

Cooperation

Cooperation

AI should not intensify zero-sum arms races

Example: International agreements on autonomous weapons

4

Attribution

Attribution

AI must be traceable to creators/controllers

Example: Clear responsibility chains for AI decisions

Intelligence Augmentation vs. Replacement

Same technology, radically different deployment choices.

Intelligence Augmentation (IA)

Goal: Make humans better at their work
Result: Human expertise becomes more valuable

Example:

AI diagnostic system → doctors make better diagnoses

Artificial Intelligence (AI)

Goal: Replace human performance
Result: Human expertise becomes obsolete

Example:

Fully automated diagnostic → radiologists unnecessary

High-Stakes vs. Low-Stakes Systems

Not all decisions should be algorithmic. Stakes determine whether human judgment is required.

Low-Stakes Systems

Algorithmic decisions acceptable

  • Entertainment recommendations
  • Ad targeting
  • Search results
  • Social media feeds
  • Shopping suggestions

High-Stakes Systems

Human judgment required

  • Criminal sentencing
  • Employment decisions
  • Healthcare diagnosis
  • Loan lending
  • University admissions

Professional Expertise Framework

Professional expertise includes more than technical knowledge:

What AI Can Help With:

  • Technical knowledge
  • Pattern recognition
  • Data analysis
  • Routine tasks

What Requires Humans:

  • Contextual judgment
  • Ethical reasoning
  • Relationships & trust
  • Wisdom & values

Applying the Four Laws in Practice

Pasquale's framework becomes powerful when you use it to evaluate the AI tools you encounter daily. Here's how each law applies to real-world AI products.

Law 1 in Action: Complementarity

Good Example

GitHub Copilot suggests code completions, but the developer decides what to accept, modify, or reject. The developer's expertise becomes more productive, not obsolete.

Concerning Example

Fully automated hiring systems that screen and reject candidates without human review. The recruiter's contextual judgment is eliminated, not augmented.

Law 2 in Action: Authenticity

Good Example

ChatGPT clearly identifies itself as an AI assistant. Users know they're interacting with a machine and can calibrate their trust accordingly.

Concerning Example

Deepfake voice cloning used in phone scams, impersonating real people. The AI counterfeits humanity to deceive and manipulate.

Your Evaluation Framework

When evaluating any AI tool, ask these four questions — one for each law:

1Complementarity: Does this tool make me better at my work, or does it try to replace my judgment entirely?
2Authenticity: Is it clear when I'm interacting with AI vs. a human? Does the tool disclose its AI nature?
3Cooperation: Does this tool contribute to a healthy ecosystem, or does it create winner-take-all dynamics?
4Attribution: Can I trace AI decisions back to responsible parties? Is there accountability?

Apply It: Evaluate Your AI Tools

  1. 1List 3 AI tools you use regularly (e.g. ChatGPT, Copilot, Grammarly, image generators).
  2. 2For each tool, classify it: Does it practice Intelligence Augmentation (makes you better) or AI Replacement (does the work for you)? Evaluate GitHub Copilot
  3. 3Apply the stakes framework: For which of your tasks is the AI making high-stakes decisions? Are those decisions getting human review?
  4. 4Score each tool against Pasquale's four laws (1-5 each): Complementarity, Authenticity, Cooperation, Attribution.
  5. 5Identify one tool where you could shift from "replacement mode" to "augmentation mode" by changing how you use it.
Reflect: Think about your profession: What expertise do you have that AI cannot replicate? How could AI tools be redesigned to enhance that expertise rather than bypass it?

Frequently Asked Questions

What are Pasquale's Four Laws?
1) Complementarity: AI should augment, not replace. 2) Authenticity: AI should not counterfeit humanity. 3) Cooperation: AI should not intensify arms races. 4) Attribution: AI must be traceable to responsible parties. These laws govern humans developing AI, not robots themselves.
What's the difference between IA and AI?
Intelligence Augmentation (IA) makes humans better at their work—expertise becomes more valuable. Artificial Intelligence (AI) as replacement makes humans obsolete—expertise is eliminated. Same technology, radically different deployment choices.
Which decisions should remain human?
High-stakes decisions affecting human lives: criminal sentencing, employment, healthcare, lending, admissions. These require context, values, mercy, and accountability that algorithms cannot provide. Low-stakes decisions (recommendations, ads) can be algorithmic.
Why does expertise matter?
Professional expertise includes: technical knowledge (AI can help), contextual judgment (AI struggles), ethical reasoning (AI cannot do), relationships (AI lacks), and wisdom (AI doesn't have). Eliminating expertise eliminates what makes professions valuable.
How do we ensure AI augments rather than replaces?
Policy intervention: regulations incentivizing IA over AI, labor protections for professionals, democratic input into automation decisions, breaking corporate monopoly on technological choices. Automation isn't inevitable—it's a political choice.

Key Insights: What You've Learned

1

Pasquale's four laws for AI governance provide essential principles: complementarity (augment human expertise, don't replace it), authenticity (don't counterfeit humanity), cooperation (avoid zero-sum arms races), and attribution (ensure traceability and accountability)—these laws preserve human dignity and meaningful work.

2

Apply these laws by distinguishing high-stakes from low-stakes AI systems, preserving human expertise in critical domains, avoiding deceptive AI that mimics humans, and ensuring transparency and accountability—effective AI governance requires balancing innovation with human values.

3

Build AI systems that enhance rather than diminish human agency: use AI for augmentation in high-stakes contexts, maintain human oversight, preserve meaningful work, and design systems that respect human expertise—the goal is intelligence augmentation, not replacement.

Test Your Knowledge

Complete this quiz to test your understanding of Pasquale's four laws for AI governance.

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