Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Hazel Si Min Lim,Araz Taeihagh

doi:10.3390/su11205791

Abstract

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.

Highlights

Smart and sustainable cities have been increasingly emphasised around the world to resolve the challenges associated with rapid urbanisation and population growth [1,2,3,4]
Our search revealed that bias, ethics, and perverse incentives were among the most prominent concerns being raised in the literature regarding algorithmic decision-making in autonomous vehicles (AVs) that have significant implications for safety and discrimination
We utilised mainly peer-reviewed journal articles to analyse the issues from algorithmic decision-making in AVs and supplemented the analysis on technical issues with conference proceedings pertaining to Artificial intelligence (AI), ML and engineering, such as those published by the Institute of Electrical and Electronics Engineers (IEEE) and Association for the Advancement of Artificial Intelligence (AAAI)

Summary

Introduction

Smart and sustainable cities have been increasingly emphasised around the world to resolve the challenges associated with rapid urbanisation and population growth [1,2,3,4]. The quality of data retrieved by the AV’s sensors is critical for decision-making [21], and the efficiency, precision and reliability of decision-making algorithms allows AVs to surpass the typical human driver in performing driving tasks [22]. The implementation of vehicles at Levels 4 and 5 is possible due to rapid technological advancements in hardware and software systems—sensor-fusion technology and computer vision allow the vehicle to detect, trace and manoeuvre safely around obstacles and under a wide range of environmental conditions [42,43]; High-performance computing enables the vehicle to process vast amounts of data to understand its environment and make spontaneous driving decisions; and communication technologies enable the vehicle to exchange information with and learn from other vehicles [44,45]. Throughout this study, we focus on vehicles classified under SAE’s Levels 4 and 5 of autonomy, which we will refer to as “AVs”

Methods

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Discussion

Conclusion