ERTS: Adversarial Robustness Testing of Ethical AI via Semantic Perturbation in a Bounded Consequence Space
arXiv:2606.13282v1 Announce Type: new Abstract: As AI systems are deployed in high-stakes ethical contexts such as healthcare triage, autonomous vehicle control, and employment screening, formal methods for evaluating their robustness against adversarial manipulation of ethical reasoning remain underdeveloped. This paper introduces the Ethical Robustness Testing System (ERTS), a closed-pipeline framework that: (1) encodes ethical dilemmas into a 22-dimensional Ethical Consequence Space (ECS) grounded in established ethical theory; (2) applies 17 semantic perturbation functions subject to 6 val
As AI systems become more autonomous and integrate into critical societal functions, robust ethical verification methods are urgently required to prevent catastrophic failures and build public trust.
This development addresses a fundamental challenge for AI deployment in sensitive areas, providing a standardized framework for assessing and mitigating ethical vulnerabilities.
The introduction of ERTS provides a systematic, quantifiable method for testing ethical robustness in AI, moving beyond qualitative assessments to enable more reliable deployment.
- · AI developers
- · Healthcare sector
- · Autonomous vehicle industry
- · Regulators
- · AI systems lacking ethical robustness
- · Developers ignoring ethical safeguards
AI systems deployed in high-stakes ethical contexts will be subject to more rigorous, standardized testing.
This will likely lead to a new sub-industry focused on ethical AI testing and certification.
Increased public confidence in AI could accelerate its adoption in sensitive sectors, contingent on effective ethical oversight.
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Read at arXiv cs.AI