Steps Toward Real-World Ethics for Self-Driving Cars: Beyond the Trolley Problem

Steps Toward Real-World Ethics for Self-Driving Cars: Beyond the Trolley Problem

Tobias Holstein, Gordana Dodig-Crnkovic, Patrizio Pelliccione
DOI: 10.4018/978-1-7998-4894-3.ch006
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Research on self-driving cars is transdisciplinary and its different aspects have attracted interest in general public debates as well as among specialists. To this day, ethical discourses are dominated by the Trolley Problem, a hypothetical ethical dilemma that is by construction unsolvable. It obfuscates much bigger real-world ethical challenges in the design, development, and operation of self-driving cars. The authors propose a systematic approach that connects processes, components, systems, and stakeholders to analyze the real-world ethical challenges for the ecology of socio-technological system of self-driving cars. They take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services, and they present practical social and ethical challenges that must be met and that imply novel expectations for engineering in car industry.
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State Of The Art Of The Ethical Analysis For Autonomous Cars

The autonomous cars ethics analysis has been introduced through the trolley problem, which has been dominating the debate since then, and has been discussed in a huge number of publications.1

Some of the authors directly apply trolley problem scenarios, as (Islam & Rashid, 2018) who present a crash-optimization algorithm that takes the number, age, and gender of people as input to assess the outcomes in case of an inescapable accident. Noothigattu et al. (2018) use the collected data from the Moral Machine Experiment (MIT Moral Machine Lab, 2016) to implement a decision-making algorithm, while Kim et al. are introducing a computational model by learning and generalizing from moral judgments of humans (2018). Numerous publications suggest implementing moral principles into algorithms of self-driving cars to address ethical issues (Dennis et al., 2016, 2014; Goodall, 2016).

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