This paper discusses algorithmic resignation, a strategic approach for
managing the use of AI systems within organizations. Algorithmic resignation
involves the deliberate and informed disengagement from AI assistance in
certain scenarios, by embedding governance mechanisms directly into AI systems.
Our proposal is not merely about disuse of AI but includes guiding when and how
these systems should be used or avoided. We discuss the multifaceted benefits
of algorithmic resignation, spanning economic efficiency, reputational gains,
and legal compliance. Further, we outline the operationalization of resignation
through various methods such as positive and negative nudges, stakeholder
incentive alignment, and careful consideration of the level of AI engagement.
Using techniques like barring access to AI outputs selectively or providing
explicit disclaimers on system performance, algorithmic resignation not only
mitigates risks associated with AI but also leverages its benefits, ensuring
the responsible and effective use of AI systems.

本文讨论了算法性辞职，一种在组织中管理使用人工智能系统的策略性方法，包括通过将治理机制直接嵌入到人工智能系统中，在某些情况下有意识且经过明确了解地与人工智能辅助工具脱钩。我们的提议不仅仅涉及对人工智能的不使用，还包括指导何时以及如何使用或避免使用这些系统。我们讨论了算法性辞职的多方面利益，包括经济效率、声誉增益和法律合规性。此外，我们还概述了通过积极和消极的推动力、利益相关者激励体系和仔细考虑人工智能参与水平等各种方法来实现辞职。通过选择性地限制对人工智能输出的访问或对系统性能提供明确的免责声明等技术手段，算法性辞职不仅可以减轻与人工智能相关的风险，而且可以利用其优势，确保人工智能系统的负责任和有效使用。

算法何时应辞职？

When Should Algorithms Resign?

In many applications of forensic image analysis, state-of-the-art results are
nowadays achieved with machine learning methods. However, concerns about their
reliability and opaqueness raise the question whether such methods can be used
in criminal investigations. So far, this question of legal compliance has
hardly been discussed, also because legal regulations for machine learning
methods were not defined explicitly. To this end, the European Commission
recently proposed the artificial intelligence (AI) act, a regulatory framework
for the trustworthy use of AI. Under the draft AI act, high-risk AI systems for
use in law enforcement are permitted but subject to compliance with mandatory
requirements. In this paper, we review why the use of machine learning in
forensic image analysis is classified as high-risk. We then summarize the
mandatory requirements for high-risk AI systems and discuss these requirements
in light of two forensic applications, license plate recognition and deep fake
detection. The goal of this paper is to raise awareness of the upcoming legal
requirements and to point out avenues for future research.

本研究回顾并总结了机器学习在法证图像分析中的高风险性质，概述了高风险人工智能系统的强制要求，并讨论了这些要求在两个法证应用（车牌识别和深度伪造检测）中的实际应用。目的是提高人们对即将发布的法律要求的认识，并指出未来研究的方向。