A Genetic Approach to the Ethical Knob

Iacca, Giovanni; Lagioia, Francesca; Loreggia, Andrea; Sartor, Giovanni

doi:10.3233/faia200854

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The car could only make computations based on data acquired by its sensors, but the ethical choices would have actually been already made by the designers. Even deciding that the passengers can customise ethical choices, or to let the system learn [90], for example in simulations, to determine values to be optimized is a negation of what ethical deliberation is. Indeed this would entail an a priori decision on a situation to come, or to decide that ethical deliberation is based on statistics of past actions.…”

Section: Ethical Questionsmentioning

confidence: 99%

Human-centered AI and robotics

et al. 2022

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Robotics has a special place in AI as robots are connected to the real world and robots increasingly appear in humans everyday environment, from home to industry. Apart from cases were robots are expected to completely replace them, humans will largely benefit from real interactions with such robots. This is not only true for complex interaction scenarios like robots serving as guides, companions or members in a team, but also for more predefined functions like autonomous transport of people or goods. More and more, robots need suitable interfaces to interact with humans in a way that humans feel comfortable and that takes into account the need for a certain transparency about actions taken. The paper describes the requirements and state-of-the-art for a human-centered robotics research and development, including verbal and non-verbal interaction, understanding and learning from each other, as well as ethical questions that have to be dealt with if robots will be included in our everyday environment, influencing human life and societies.

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Section: Ethical Questionsmentioning

confidence: 99%

Human-centered AI and robotics

et al. 2022

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When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data

Awad,

Levine,

Loreggia

et al. 2024

Auton Agent Multi-Agent Syst

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Constraining the actions of AI systems is one promising way to ensure that these systems behave in a way that is morally acceptable to humans. But constraints alone come with drawbacks as in many AI systems, they are not flexible. If these constraints are too rigid, they can preclude actions that are actually acceptable in certain, contextual situations. Humans, on the other hand, can often decide when a simple and seemingly inflexible rule should actually be overridden based on the context. In this paper, we empirically investigate the way humans make these contextual moral judgements, with the goal of building AI systems that understand when to follow and when to override constraints. We propose a novel and general preference-based graphical model that captures a modification of standard dual process theories of moral judgment. We then detail the design, implementation, and results of a study of human participants who judge whether it is acceptable to break a well-established rule: no cutting in line. We then develop an instance of our model and compare its performance to that of standard machine learning approaches on the task of predicting the behavior of human participants in the study, showing that our preference-based approach more accurately captures the judgments of human decision-makers. It also provides a flexible method to model the relationship between variables for moral decision-making tasks that can be generalized to other settings.

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A Genetic Approach to the Ethical Knob

Cited by 2 publications

References 15 publications

Human-centered AI and robotics

Human-centered AI and robotics

When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data

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