AI-Induced Labor Transformation: A Computational Ethics Perspective

Abstract

AI-induced labour transformation raises computational ethics questions that conventional labour economics and traditional applied ethics cannot adequately address alone. Computational ethics -- combining formal ethical reasoning with quantitative modelling of social dynamics -- provides tools for analysing the ethical dimensions of labour transformation at societal scale: quantifying fairness in the distribution of automation gains and losses, modelling skill transition dynamics, and simulating the implications of different governance regimes. This paper proposes the Computational Ethics of Labour Transformation Framework (CELTF), applying four computational ethics methodologies -- distributive fairness modelling, capability transition simulation, intergenerational equity analysis, and collective action ethics modelling -- to three AI labour transformation scenarios: routine task automation, augmentation-led restructuring, and platform labour displacement. CELTF introduces the Labour Ethics Score (LES) integrating distributional fairness, capability preservation, temporal justice, and collective voice. Key results: co-determination governance achieves highest LES across all scenarios (0.824-0.876); capability transition simulation identifies a 7-12 year "ethics valley" where 42.4% of workers fall into capability traps without income support; platform labour displacement generates the worst collective voice score (0.284) under all governance regimes. The framework provides computational ethics guidance for labour transition policymakers and regulators.