# Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities

arXiv: https://arxiv.org/abs/1910.13122 · HTML: https://mlsanity.com/paper/1910.13122/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions](https://mlsanity.com/paper/2503.00940/) (2025)
- [Moral Dilemmas for Moral Machines](https://mlsanity.com/paper/2203.06152/) (2022)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [The Risks of Machine Learning Systems](https://mlsanity.com/paper/2204.09852/) (2022)
- [Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems](https://mlsanity.com/paper/2408.15550/) (2024)
- [Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling](https://mlsanity.com/paper/2212.02908/) (2022)
- [Robust Estimation of Surface Curvature Information from Point Cloud Data](https://mlsanity.com/paper/2306.00299/) (2023)

## Abstract (arXiv)

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.