# Software Doping Analysis for Human Oversight

arXiv: https://arxiv.org/abs/2308.06186 · HTML: https://mlsanity.com/paper/2308.06186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 152 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 26 of 26)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making](https://mlsanity.com/paper/1806.01059/) (2018)
- [Two Simple Ways to Learn Individual Fairness Metrics from Data](https://mlsanity.com/paper/2006.11439/) (2020)
- [Metric Learning for Individual Fairness](https://mlsanity.com/paper/1906.00250/) (2019)
- [Verifying Individual Fairness in Machine Learning Models](https://mlsanity.com/paper/2006.11737/) (2020)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Fairness in Ranking: A Survey](https://mlsanity.com/paper/2103.14000/) (2021)
- [Bias and Discrimination in AI: a cross-disciplinary perspective](https://mlsanity.com/paper/2008.07309/) (2020)
- [Temporal Logics for Hyperproperties](https://mlsanity.com/paper/1401.4492/) (2014)
- [On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness](https://mlsanity.com/paper/2108.05379/) (2021)
- [Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue](https://mlsanity.com/paper/2108.03012/) (2021)
- [Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives](https://mlsanity.com/paper/2108.07711/) (2021)
- [Doping Tests for Cyber-Physical Systems](https://mlsanity.com/paper/1904.08641/) (2019)
- [Is your software on dope? Formal analysis of surreptitiously "enhanced" programs](https://mlsanity.com/paper/1702.04693/) (2017)
- [Facets of Software Doping](https://mlsanity.com/paper/1803.10154/) (2018)
- [Conformance Relations and Hyperproperties for Doping Detection in Time and Space](https://mlsanity.com/paper/2012.03910/) (2020)
- [RTLola on Board: Testing Real Driving Emissions on your Phone](https://mlsanity.com/paper/2111.05255/) (2021)

## Built on by (most cited first, 1 of 1)

- [Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework](https://mlsanity.com/paper/2501.08046/) (2025)

## Abstract (arXiv)

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.