Ethical Decision Support Systems for Autonomous Vehicles - Integrating Human Values and Computational Intelligence: Develops ethical decision support systems for AVs by integrating human values and computational intelligence
Keywords:
Ethical Frameworks, Transparency, ExplainabilityAbstract
The widespread adoption of autonomous vehicles (AVs) hinges on their ability to navigate complex traffic scenarios while adhering to ethical principles. However, current AV technology often lacks the ability to make nuanced decisions in unavoidable accident situations. This research paper proposes the development of Ethical Decision Support Systems (EDSS) for AVs, integrating human values and computational intelligence.
The paper begins by outlining the ethical dilemmas faced by AVs in unavoidable accident scenarios. It discusses various philosophical frameworks for ethical decision-making, such as utilitarianism, which prioritizes minimizing overall harm, and deontology, which emphasizes adherence to moral rules. The limitations of applying these frameworks directly to AV programming are explored, highlighting the need for a more nuanced approach.
Next, the paper introduces the concept of human values in AV decision-making. It explores methods for incorporating these values into the EDSS framework. This may involve public surveys, focus groups, and stakeholder consultations to establish a baseline for societal ethical preferences. The paper then delves into the realm of computational intelligence, exploring techniques like machine learning and artificial neural networks that can be leveraged by the EDSS.
A crucial aspect of the paper is the integration of human values and computational intelligence within the EDSS. The paper proposes a multi-layered architecture for the EDSS, where the initial layer processes sensor data and identifies potential accident scenarios. The subsequent layer leverages machine learning algorithms to assess the severity of potential outcomes based on the established ethical framework. Finally, a human values module, informed by public preferences, influences the final decision within a pre-defined range of acceptable actions.
The paper emphasizes the importance of transparency and explainability in the EDSS. It proposes methods for logging and auditing decisions made by the system, allowing for human oversight and potential intervention in exceptional circumstances. The paper also addresses legal and regulatory considerations surrounding the implementation of EDSS in AVs. It explores potential legal frameworks for assigning liability in accident scenarios involving AVs with EDSS.
Finally, the paper discusses the societal implications of EDSS in AVs. It explores the potential for increased public trust and acceptance of autonomous technology. Additionally, the paper addresses potential challenges such as bias in the data used to train the machine learning algorithms and the need for ongoing public discourse on evolving ethical considerations.
The research concludes by outlining the potential benefits and challenges associated with EDSS in AVs. It emphasizes the importance of continued research and development to refine the system and ensure its responsible implementation for a safer and more ethical future of autonomous transportation.
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