This article proposes a novel integrative approach to moral judgment and a related model that could explain how unconscious heuristic processes are transformed into consciously accessible moral intuitions. Different hypothetical cases have been tested empirically to evoke moral intuitions that support principles from competing moral theories. We define and analyze the types of intuitions that moral theories and studies capture: those focusing on agents (A), deeds (D), and consequences (C). The integrative ADC approach uses the heuristic principle of “attribute substitution” (...) to explain how people make intuitive judgments. The target attributes of moral judgments are moral blameworthiness and praiseworthiness, which are substituted with more accessible and computable information about an agent's virtues and vices, right/wrong deeds, and good/bad consequences. The processes computing this information are unconscious and inaccessible, and therefore explaining how they provide input for moral intuitions is a key problem. We analyze social heuristics identified in the literature and offer an outline for a new model of moral judgment. Simple social heuristics triggered by morally salient cues rely on three distinct processes (role-model entity, action analysis, and consequence tallying—REACT) in order to compute the moral valence of specific intuitive responses (A, D, and C). These are then rapidly combined to form an intuitive judgment that could guide quick decision making. The ADC approach and REACT model can clarify a wide set of data from empirical moral psychology and could inform future studies on moral judgment, as well as case assessments and discussions about issues causing “deadlocked” moral intuitions. (shrink)

What is the ethical impact of artificial intelligence assistants on human lives, and specifically how much do they threaten our individual autonomy? Recently, as part of forming an ethical framework for thinking about the impact of AI assistants on our lives, John Danaher claims that if the external automaticity generated by the use of AI assistants threatens our autonomy and is therefore ethically problematic, then the internal automaticity we already live with should be viewed in the same way. He takes (...) advantage of this paradox of internal automaticity to downplay the threats of external automaticity to our autonomy. We respond in this paper by challenging the legitimacy of the paradox. While Danaher assumes that internal and external automaticity are roughly equivalent, we argue that there are reasons why we should accept a large degree of internal automaticity, that it is actually essential to our sense of autonomy, and as such it is ethically good; however, the same does not go for external automaticity. Therefore, the similarity between the two is not as powerful as the paradox presumes. In conclusion, we make practical recommendations for how to better manage the integration of AI assistants into society. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Autonomous vehicles —and accidents they are involved in—attest to the urgent need to consider the ethics of artificial intelligence. 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 model :3–20, 2014a, Behav Brain Sci 37:487–488, 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 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. (shrink)