Toward Implementing the ADC Model of Moral Judgment in Autonomous Vehicles.
Autonomous vehicles (AVs)-and accidents they are involved in-attest to the urgent need to consider the ethics of artificial intelligence (AI). The question dominating the discussion so far has been whether we want AVs to behave in a 'selfish' or utilitarian manner. Rather than considering modeling self-driving cars on a single moral system like utilitarianism, one possible way to approach programming for AI would be to reflect recent work in neuroethics. The agent-deed-consequence (ADC) model (Dubljević and Racine in AJOB Neurosci 5(4):3-20, 2014a, Behav Brain Sci 37(5):487-488, 2014b) provides a promising descriptive and normative account while also lending itself well to implementation in AI. The ADC model explains moral judgments by breaking them down into positive or negative intuitive evaluations of the agent, deed, and consequence in any given situation. These intuitive evaluations combine to produce a positive or negative judgment of moral acceptability. For example, the overall judgment of moral acceptability in a situation in which someone committed a deed that is judged as negative (e.g., breaking a law) would be mitigated if the agent had good intentions and the action had a good consequence. This explains the considerable flexibility and stability of human moral judgment that has yet to be replicated in AI. This paper examines the advantages and disadvantages of implementing the ADC model and how the model could inform future work on ethics of AI in general.
['Dubljević|Veljko|V|']
D001185:Artificial Intelligence D001334:Automobile Driving D028663:Ethical Theory D006801:Humans D007600:Judgment D009014:Morals
Artificial intelligence (AI) Neuroethics Agent–deed–consequence (ADC) model Artificial morality Artificial neural networks Autonomous vehicles (AVs)