发表机构
George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对金融平台注册易注销难的不对称暗黑模式，本文提出以努力成本约束定义公平性，证明退出成本不高于加入成本可通过计数验证，并在移动信用卡原型中实现公平选择架构。
AI 中文摘要
数字金融平台让注册变得轻而易举，却让注销变得困难重重。这种不对称性是一种暗黑模式，操纵着那些已经决定离开的用户。现有研究在部署后识别此类模式，监管机构在损害发生后对其进行制裁，但两者都没有为设计师提供构建避免操纵的界面的标准。我们将服务提供者建模为以努力为工具的对手，并将公平性表达为一种约束，要求离开的成本永远不超过加入的成本。将交互成本定义为导航步骤、强制输入和确认提示的函数，我们证明：当且仅当退出流程的每个组成部分都不超过进入流程的对应部分时，该约束对每一种努力权重分配都成立。因此，公平性可以通过计数而非估算认知努力来验证，且完全等价并非必要，因为退出合法地需要比进入更少的输入。我们将该模型与视觉对等和语言中立的恒等式一起，实例化在一个具有并行注册和注销流程的移动信用卡原型中。
英文摘要
Digital financial platforms make enrollment effortless and cancellation laborious. This asymmetry is a dark pattern that manipulates users who have already decided to leave. Existing work identifies such patterns after deployment, and regulators sanction them after harm, yet neither provides designers with a criterion for building interfaces that avoid manipulation. We model the provider as an adversary whose instrument is effort and express fairness as a constraint requiring that leaving never cost more than joining. Defining interaction cost over navigation steps, mandatory inputs, and confirmation prompts, we prove that this constraint holds for every assignment of effort weights if and only if no component of the exit flow exceeds its counterpart at entry. Fairness is therefore verifiable by counting rather than by estimating cognitive effort, and exact equivalence is unnecessary because exit legitimately requires fewer inputs than entry. We instantiate the model, together with invariants for visual parity and linguistic neutrality, in a mobile credit card prototype with parallel sign-up and cancellation workflows.
CommentsAccepted at HumSec Workshop, ESORICS '26
