Abstract 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.

Recent research efforts strive to aid in designing explainable systems. Nevertheless, a systematic and overarching approach to ensure explainability by design is still missing. Often it is not even clear what precisely is meant when demanding explainability. To address this challenge, we investigate the elicitation, specification, and verification of explainablity as a Non-Functional Requirement (NFR) with the long-term vision of establishing a standardized certification process for the explainability of software-driven systems in tandem with appropriate development techniques. In this work, we carve out different notions of explainability and high-level requirements people have in mind when demanding explainability, and sketch how explainability concerns may be approached in a hypothetical hiring scenario. We provide a conceptual analysis which unifies the different notions of explainability and the corresponding explainability demands.

A new method for timing recovery and delay-spread estimation in OFDM systems is proposed. The method uses the correlation between the cyclic extension and data portion of the received OFDM symbols to estimate timing. Although cyclic extension based timing estimation methods have been proposed earlier, the present scheme is unique in the way the correlation is computed and used to estimate timing. The method identifies the complete range of ISI-free sampling positions available in an OFDM symbol, instead of identifying just one of them randomly. Further, the delay-spread information provided by the method can be used to improve the BER performance of the system by adjusting the frequency-domain interpolation filter bandwidth.