This article introduces a framework that is meant to assist in mitigating
societal risks that software can pose. Concretely, this encompasses facets of
software doping as well as unfairness and discrimination in high-risk
decision-making systems. The term software doping refers to software that
contains surreptitiously added functionality that is against the interest of
the user. A prominent example of software doping are the tampered emission
cleaning systems that were found in millions of cars around the world when the
diesel emissions scandal surfaced. The first part of this article combines the
formal foundations of software doping analysis with established probabilistic
falsification techniques to arrive at a black-box analysis technique for
identifying undesired effects of software. We apply this technique to emission
cleaning systems in diesel cars but also to high-risk systems that evaluate
humans in a possibly unfair or discriminating way. We demonstrate how our
approach can assist humans-in-the-loop to make better informed and more
responsible decisions. This is to promote effective human oversight, which will
be a central requirement enforced by the European Union's upcoming AI Act. We
complement our technical contribution with a juridically, philosophically, and
psychologically informed perspective on the potential problems caused by such
systems.

本文介绍了一个框架，旨在帮助减轻软件可能带来的社会风险。具体而言，包括软件兴奋剂以及高风险决策系统中的不公平和歧视方面。