Scholarlink

<p>Emotional artificial intelligence (AI)—systems that infer, simulate, or influence human feelings—create ethical risks that existing frameworks of privacy, transparency, and oversight cannot fully address. This paper advances the concept of <i>Affective Sovereignty</i>: the right of individuals to remain the ultimate interpreters of their own emotions. We make four contributions. First, we develop <i>formal foundations</i> by decomposing risk functions to capture interpretive override as a measurable cost. Second, we propose a <i>Sovereign-by-Design</i> architecture that embeds safeguards and contestability into the machine learning lifecycle. Third, we operationalize sovereignty through new metrics—the Interpretive Override Score (IOS), After-correction Misalignment Rate (AMR), and Affective Divergence (AD)—and demonstrate their use in a proof-of-concept simulation. Fourth, we link technical design to governance by introducing the <i>Affective Sovereignty Contract (ASC)</i>, a machine-readable policy layer, and by issuing a <i>Declaration of Affective Sovereignty</i> as a normative anchor for regulation. Together, these elements offer a computational framework for aligning emotional AI with human dignity and autonomy, moving beyond abstract principles toward enforceable, testable standards. In proof-of-mechanism simulations with <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(k=10\)</EquationSource> </InlineEquation> random seeds, enforcing DRIFT (Dynamic Risk and Interpretability Feedback Throttling) with policy constraints reduces the Interpretive Override Score (IOS) from <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(32.4\%\pm 3.8\)</EquationSource> </InlineEquation> (baseline) to <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(14.1\%\pm 2.9\)</EquationSource> </InlineEquation>, demonstrating measurable preservation of affective sovereignty with quantified variability. Results reported here are based on proof-of-mechanism simulations; a preregistered human-subject evaluation (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(n=48\)</EquationSource> </InlineEquation>) is planned and has not yet been conducted.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Formal and computational foundations for implementing Affective Sovereignty in emotion AI systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月17日

查看全文
  • Ryan SangBaek Kim

摘要

Emotional artificial intelligence (AI)—systems that infer, simulate, or influence human feelings—create ethical risks that existing frameworks of privacy, transparency, and oversight cannot fully address. This paper advances the concept of Affective Sovereignty: the right of individuals to remain the ultimate interpreters of their own emotions. We make four contributions. First, we develop formal foundations by decomposing risk functions to capture interpretive override as a measurable cost. Second, we propose a Sovereign-by-Design architecture that embeds safeguards and contestability into the machine learning lifecycle. Third, we operationalize sovereignty through new metrics—the Interpretive Override Score (IOS), After-correction Misalignment Rate (AMR), and Affective Divergence (AD)—and demonstrate their use in a proof-of-concept simulation. Fourth, we link technical design to governance by introducing the Affective Sovereignty Contract (ASC), a machine-readable policy layer, and by issuing a Declaration of Affective Sovereignty as a normative anchor for regulation. Together, these elements offer a computational framework for aligning emotional AI with human dignity and autonomy, moving beyond abstract principles toward enforceable, testable standards. In proof-of-mechanism simulations with \(k=10\) random seeds, enforcing DRIFT (Dynamic Risk and Interpretability Feedback Throttling) with policy constraints reduces the Interpretive Override Score (IOS) from \(32.4\%\pm 3.8\) (baseline) to \(14.1\%\pm 2.9\) , demonstrating measurable preservation of affective sovereignty with quantified variability. Results reported here are based on proof-of-mechanism simulations; a preregistered human-subject evaluation ( \(n=48\) ) is planned and has not yet been conducted.