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The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?

Research output: Working paper

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The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance? / Kaivanto, Kim.
Lancaster: Lancaster University, Department of Economics, 2025. (Economics Working Papers Series).

Research output: Working paper

Harvard

Kaivanto, K 2025 'The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?' Economics Working Papers Series, Lancaster University, Department of Economics, Lancaster.

APA

Kaivanto, K. (2025). The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance? (Economics Working Papers Series). Lancaster University, Department of Economics.

Vancouver

Kaivanto K. The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance? Lancaster: Lancaster University, Department of Economics. 2025 May 12. (Economics Working Papers Series).

Author

Kaivanto, Kim. / The Precautionary Principle and the Innovation Principle : Incompatible Guides for AI Innovation Governance?. Lancaster : Lancaster University, Department of Economics, 2025. (Economics Working Papers Series).

Bibtex

@techreport{fe26e174bb0b45c089ff41d6bc253be2,
title = "The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?",
abstract = "In policy debates concerning the governance and regulation of Artificial Intelligence (AI), both the Precautionary Principle (PP) and the Innovation Principle (IP) are advocated by their respective interest groups. Do these principles offer wholly incompatible and contradictory guidance? Does one necessarily negate the other? I argue here that provided attention is restricted to weak-form PP and IP, the answer to both of these questions is “No.” The essence of these weak formulations is the requirement to fully account for type-I error costs arising from erroneously preventing the innovation{\textquoteright}s diffusion through society (i.e. mistaken regulatory redlighting) as well as the type-II error costs arising from erroneously allowing the innovation to diffuse through society (i.e. mistaken regulatory green-lighting). Within the Signal Detection Theory (SDT) model developed here, weak-PP red-light (weak-IP green-light) determinations are optimal for sufficiently small (large) ratios of expected type-I to type-II error costs. For intermediate expected cost ratios, an amber-light {\textquoteleft}wait-and-monitor{\textquoteright} policy is optimal. Regulatory sandbox instruments allow AI testing and experimentation to take place within a structured environment of limited duration and societal scale, whereby the expected cost ratio falls within the {\textquoteleft}wait-and-monitor{\textquoteright} range. Through sandboxing regulators and innovating firms learn more about the expected cost ratio, and what respective adaptations — of regulation, of technical solution, of business model, or combination thereof, if any — are needed to keep the ratio out of the weak-PP red-light zone.",
keywords = "artificial intelligence, foundational AI, general-purpose AI systems, AI governance, precautionary principle, innovation principle, countervailing risk, scientific uncertainty, signal detection theory, misclassification costs, discriminability, ROC curve, de minimis risk, trust and polarization, protected values, non-comparable values, continuity axiom, regulatory sandboxes",
author = "Kim Kaivanto",
year = "2025",
month = may,
day = "12",
language = "English",
series = "Economics Working Papers Series",
publisher = "Lancaster University, Department of Economics",
type = "WorkingPaper",
institution = "Lancaster University, Department of Economics",

}

RIS

TY - UNPB

T1 - The Precautionary Principle and the Innovation Principle

T2 - Incompatible Guides for AI Innovation Governance?

AU - Kaivanto, Kim

PY - 2025/5/12

Y1 - 2025/5/12

N2 - In policy debates concerning the governance and regulation of Artificial Intelligence (AI), both the Precautionary Principle (PP) and the Innovation Principle (IP) are advocated by their respective interest groups. Do these principles offer wholly incompatible and contradictory guidance? Does one necessarily negate the other? I argue here that provided attention is restricted to weak-form PP and IP, the answer to both of these questions is “No.” The essence of these weak formulations is the requirement to fully account for type-I error costs arising from erroneously preventing the innovation’s diffusion through society (i.e. mistaken regulatory redlighting) as well as the type-II error costs arising from erroneously allowing the innovation to diffuse through society (i.e. mistaken regulatory green-lighting). Within the Signal Detection Theory (SDT) model developed here, weak-PP red-light (weak-IP green-light) determinations are optimal for sufficiently small (large) ratios of expected type-I to type-II error costs. For intermediate expected cost ratios, an amber-light ‘wait-and-monitor’ policy is optimal. Regulatory sandbox instruments allow AI testing and experimentation to take place within a structured environment of limited duration and societal scale, whereby the expected cost ratio falls within the ‘wait-and-monitor’ range. Through sandboxing regulators and innovating firms learn more about the expected cost ratio, and what respective adaptations — of regulation, of technical solution, of business model, or combination thereof, if any — are needed to keep the ratio out of the weak-PP red-light zone.

AB - In policy debates concerning the governance and regulation of Artificial Intelligence (AI), both the Precautionary Principle (PP) and the Innovation Principle (IP) are advocated by their respective interest groups. Do these principles offer wholly incompatible and contradictory guidance? Does one necessarily negate the other? I argue here that provided attention is restricted to weak-form PP and IP, the answer to both of these questions is “No.” The essence of these weak formulations is the requirement to fully account for type-I error costs arising from erroneously preventing the innovation’s diffusion through society (i.e. mistaken regulatory redlighting) as well as the type-II error costs arising from erroneously allowing the innovation to diffuse through society (i.e. mistaken regulatory green-lighting). Within the Signal Detection Theory (SDT) model developed here, weak-PP red-light (weak-IP green-light) determinations are optimal for sufficiently small (large) ratios of expected type-I to type-II error costs. For intermediate expected cost ratios, an amber-light ‘wait-and-monitor’ policy is optimal. Regulatory sandbox instruments allow AI testing and experimentation to take place within a structured environment of limited duration and societal scale, whereby the expected cost ratio falls within the ‘wait-and-monitor’ range. Through sandboxing regulators and innovating firms learn more about the expected cost ratio, and what respective adaptations — of regulation, of technical solution, of business model, or combination thereof, if any — are needed to keep the ratio out of the weak-PP red-light zone.

KW - artificial intelligence

KW - foundational AI

KW - general-purpose AI systems

KW - AI governance

KW - precautionary principle

KW - innovation principle

KW - countervailing risk

KW - scientific uncertainty

KW - signal detection theory

KW - misclassification costs

KW - discriminability

KW - ROC curve

KW - de minimis risk

KW - trust and polarization

KW - protected values

KW - non-comparable values

KW - continuity axiom

KW - regulatory sandboxes

M3 - Working paper

T3 - Economics Working Papers Series

BT - The Precautionary Principle and the Innovation Principle

PB - Lancaster University, Department of Economics

CY - Lancaster

ER -