Retention and Transfer of Cognitive Bias Mitigation Interventions: A Systematic Literature Study.

Cognitive biases can adversely affect human judgment and decision making and should therefore preferably be mitigated, so that we can achieve our goals as effectively as possible. Hence, numerous bias mitigation interventions have been developed and evaluated. However, to be effective in practical situations beyond laboratory conditions, the bias mitigation effects of these interventions should be retained over time and should transfer across contexts. This systematic review provides an overview of the literature on retention and transfer of bias mitigation interventions. A systematic search yielded 52 studies that were eligible for screening. At the end of the selection process, only 12 peer-reviewed studies remained that adequately studied retention over a period of at least 14 days (all 12 studies) or transfer to different tasks and contexts (one study). Eleven of the relevant studies investigated the effects of bias mitigation training using game- or video-based interventions. These 11 studies showed considerable overlap regarding the biases studied, kinds of interventions, and decision-making domains. Most of them indicated that gaming interventions were effective after the retention interval and that games were more effective than video interventions. The study that investigated transfer of bias mitigation training (next to retention) found indications of transfer across contexts. To be effective in practical circumstances, achieved effects of cognitive training should lead to enduring changes in the decision maker's behavior and should generalize toward other task domains or training contexts. Given the small number of overlapping studies, our main conclusion is that there is currently insufficient evidence that bias mitigation interventions will substantially help people to make better decisions in real life conditions. This is in line with recent theoretical insights about the "hard-wired" neural and evolutionary origin of cognitive biases.

Human- versus Artificial Intelligence.

AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and "collaborate" with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI "partners" with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying 'psychological' mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.

Perceived demands during modern military operations.

Using a cross-sectional design, this study explored operational demands during the International Security Assistance Force for Afghanistan (2009-2010) across distinct military units. A total of 1,413 Dutch soldiers, nested within four types of units (i.e., combat, combat support, service support, and command support units) filled out a 23-item self-survey in which they were asked to evaluate the extent to which they experienced operational characteristics as demanding. Exploratory factor analysis identified six underlying dimensions of demands. Multivariate analysis of variance revealed that distinct units are characterized by their own unique constellation of perceived demands, even after controlling for previous deployment experience. Most notable findings were found when comparing combat units to other types of units. These insights can be used to better prepare different types of military units for deployment, and support them in the specific demands they face during deployment.