Volume 19, Issue 11, November 2019, Page 9-12.

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is (...) normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms. (shrink)

As a way to address both ominous and ordinary threats of artificial intelligence, researchers have started proposing ways to stop an AI system before it has a chance to escape outside control and cause harm. A so-called “big red button” would enable human operators to interrupt or divert a system while preventing the system from learning that such an intervention is a threat. Though an emergency button for AI seems to make intuitive sense, that approach ultimately concentrates on the point (...) when a system has already “gone rogue” and seeks to obstruct interference. A better approach would be to make ongoing self-evaluation and testing an integral part of a system’s operation, diagnose how the system is in error and to prevent chaos and risk before they start. In this paper, we describe the demands that recent big red button proposals have not addressed, and we offer a preliminary model of an approach that could better meet them. We argue for an ethical core that consists of a scenario-generation mechanism and a simulation environment that are used to test a system’s decisions in simulated worlds, rather than the real world. This EC would be kept opaque to the system itself: through careful design of memory and the character of the scenario, the system’s algorithms would be prevented from learning about its operation and its function, and ultimately its presence. By monitoring and checking for deviant behavior, we conclude, a continual testing approach will be far more effective, responsive, and vigilant toward a system’s learning and action in the world than an emergency button which one might not get to push in time. (shrink)

Background: The four principles of Beauchamp and Childress - autonomy, non-maleficence, beneficence and justice - havebeen extremely influential in the field of medical ethics, and are fundamental for understanding the currentapproach to ethical assessment in health care. This study tests whether these principles can be quantitativelymeasured on an individual level, and then subsequently if they are used in the decision making process whenindividuals are faced with ethical dilemmas. Methods: The Analytic Hierarchy Process was used as a tool for the measurement (...) of the principles. Four scenarios, whichinvolved conflicts between the medical ethical principles, were presented to participants and they madejudgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if theywere in the situation. Results: Individual preferences for these medical ethical principles can be measured using the Analytic HierarchyProcess. This technique provides a useful tool in which to highlight individual medical ethical values. Onaverage individuals have a significant preference for non-maleficence over the other principles, however, andperhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specificethical dilemmas. Conclusions: People state they value these medical ethical principles but they do not actually seem to use them directly in thedecision making process. The reasons for this are explained through the lack of a behavioural model to accountfor the relevant situational factors not captured by the principles. The limitations of the principles in predictingethical decision making are discussed. (shrink)

Objectives - To explore the wvay ethical principles develop during a medical education course for three groups of medical students - in their first year, at the beginning of their penultimate (fifth) year and towards the end of their final (sixth) year. Design - Survey questionnaire administered to medical students in their first, fifth and final (sixth) year. Setting - A large medical school in Queensland, Australia. Survey sample - Approximately half the students in each of three years (first, fifth (...) and sixth) provided data on a voluntary basis, a total of 385 students. Results - At the point of entry, minor differences were found between medical students and first year law and psychology students. More striking were differences between male and female medical students, suggesting early socialization had a substantial impact here. Conclusions - Results indicate that substantial changes in attitude have developed by the beginning of fifth year with little change thereafter. Gender difference persisted. Some difference in ethical attitudes were found when groups of different ethnic backgrounds were compared. The impact of a move to a graduate medical course, which gives high priority to ethics within a professional development domain, can now be evaluated. (shrink)

In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to (1) the physician’s role in the patients’ formation of and acting on personal preferences and values, (2) the bias and opacity problem of AI systems, and (3) rational concerns about the future societal effects of introducing AI systems in the health care sector.