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Research output: Working paper
Research output: Working paper
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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 -