Foresight for ethical AI.

There is growing expectation that artificial intelligence (AI) developers foresee and mitigate harms that might result from their creations; however, this is exceptionally difficult given the prevalence of emergent behaviors that occur when integrating AI into complex sociotechnical systems. We argue that Naturalistic Decision Making (NDM) principles, models, and tools are well-suited to tackling this challenge. Already applied in high-consequence domains, NDM tools such as the premortem, and others, have been shown to uncover a reasonable set of risks of underlying factors that would lead to ethical harms. Such NDM tools have already been used to develop AI that is more trustworthy and resilient, and can help avoid unintended consequences of AI built with noble intentions. We present predictive policing algorithms as a use case, highlighting various factors that led to ethical harms and how NDM tools could help foresee and mitigate such harms.

Minding Your Own Business? Mindfulness Decreases Prosocial Behavior for People With Independent Self-Construals.

Mindfulness appears to promote individual well-being, but its interpersonal effects are less clear. Two studies in adult populations tested whether the effects of mindfulness on prosocial behavior differ according to individuals' self-construals. In Study 1 (N = 366), a brief mindfulness induction, compared with a meditation control condition, led to decreased prosocial behavior among people with relatively independent self-construals but had the opposite effect among those with relatively interdependent self-construals. In Study 2 (N = 325), a mindfulness induction led to decreased prosocial behavior among people primed with independence but had the opposite effect among those primed with interdependence. The effects of mindfulness on prosocial behavior appear to depend on individuals' broader social goals. This may have implications for the increasing popularity of mindfulness training around the world.

Empathic Concern and the Desire to Help as Separable Components of Compassionate Responding.

When do people experience versus regulate responses to compassion-evoking stimuli? We hypothesized that compassionate responding is composed of two factors-empathic concern and the desire to help-and that these would be differentially affected by perspective taking and self-affirmation. Exploratory (Study 1) and confirmatory (Study 2) factor analyses indicated that a compassion measure consisted of two factors corresponding to empathic concern and the desire to help. In Study 1 ( N = 237), participants with high emotion regulation ability reported less empathic concern for multiple children than for one, but perspective taking prevented this effect. In Study 2 ( N = 155), participants reported less desire to help multiple children, but only in the presence of self-affirmation. In both the studies, empathic concern predicted greater distress while the desire to help predicted greater chances of donating. Compassionate responding may consist of two separable facets that collapse under distinct conditions and that predict distinct outcomes.