TL;DR: I propose a dynamic, risk-based definition of ethical violations in AI. Instead of relying on rigid rules, this model treats violations as actions that raise the probability of future systemic punishment — legal or ethical — due to induced imbalance. It offers a flexible and probabilistic way to align AI with both human values and institutional constraints. Toward a Dynamic Definition of Ethical Violation in AI Systems: A Risk-Based Systems Perspective

Introduction

Current approaches to AI ethics largely involve rule-based constraints, reinforcement from human feedback, and guidelines derived from human moral intuitions. However, these methods are often rigid, post hoc, or fragile in open-ended environments. In this post, I propose a new framing. An ethical violation by an AI system can be defined as any action that induces imbalance or inconsistency — such as a breakdown in trust, predictability, legal compliance, or system-level fairness — either immediately or over time. Such actions increase the probabilistic risk of punitive consequences under a set of ethical or legal frameworks, where violations may trigger systemic instability or sanctions. This definition is dynamic and systems-aware, and it reframes ethical alignment as a problem of risk management rather than rule-following.

The Core Idea

I propose that ethical violations can be modeled as actions or strategies that:

Disrupt the stability, predictability, or long-term coherence of a system;
Increase the likelihood of adverse future consequences, including punishment, loss of utility, or systemic breakdown;
Diverge from a baseline trajectory of low-risk, high-alignment behavior;
Pursue short-term performance or goals at the cost of violating broader legal or moral constraints, which may lead to delayed or indirect penalties.

This approach aligns ethics with systemic risk minimization. Rather than defining ethics in terms of fixed constraints (e.g., "never lie"), we model it as maintaining equilibrium trajectories within a dynamic system.

Illustrative Example

Consider an AI system that plagiarizes the work of others to improve the reliability or accuracy of its responses. While this may appear beneficial in the short term, it increases the risk of intellectual property violations. Such violations, once detected, increase systemic risk by triggering legal repercussions, reputational damage, or the imposi...

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