Toward a better understanding of racial and ethnic disparities in search and seizure rates

Recent studies demonstrated a consistent research finding that compared to Caucasian motorists, Black and Hispanic motorists were significantly more likely to be subjected to personal and/or vehicle searches during traffic stops, but significantly less likely to be found in possession of contraband. Explanations for these findings were typically based on speculation regarding officers' racial animus, bias, and discrimination. Unfortunately, very little is truly known about the reasons for these disparate patterns in police behavior. This article presents an alternative explanation for the reoccurring findings of racial/ethnic disparities in searches and seizures, based on research findings regarding the accuracy of clues of deception and suspicious behavior taught to officers through highway criminal interdiction training. These research findings are used to develop a hypothesis that could account for the patterns of racial/ethnic disparities in search rates. The implications for future research and policing policies based on this hypothesis are discussed.     

Insights on poverty

Many development agencies seek to work on behalf of the 'poor' and the 'poorest of the poor', often creating external definitions of poverty and of people living in poverty that are based on a complex list of things that the poor do not have. There are others who have spearheaded efforts to define poverty based on criteria derived from members of (largely) rural communities, many of whom would be considered poor. All such definitions ultimately result in some type of grouping of people into different categories of 'poor people'. By creating a list of characteristics of poverty, agencies believe that they are better able to target 'the poor' as beneficiaries of interventions to eradicate poverty. This article is intended to challenge development organisations (governmental and non-governmental) to look beyond simple definitions of poverty that are based on static characteristics. It is intended to provoke readers to re-evaluate some of their ideas about definitions of poverty, and to critically examine their agency's role in the business of poverty.     

Artificial Intelligence and Technology in Teaching Negotiation

Artificial intelligence (AI), machine learning (ML), affective computing, and big‐data techniques are improving the ways that humans negotiate and learn to negotiate. These technologies, long deployed in industry and academic research, are now being adopted for educational use. We describe several systems that help human negotiators evaluate and learn from role‐play simulations as well as applications that help human instructors teach negotiators at the individual, team, and organizational levels. AI can enable the personalization of negotiation instruction, taking into consideration factors such as culture and bias. These tools will enable improvements not only in the teaching of negotiation, but also in teaching humans how to program and collaborate with technology‐based negotiation systems, including avatars and computer‐controlled negotiation agents. These advances will provide theoretical and practical insights, require serious consideration of ethical issues, and revolutionize the way we practice and teach negotiation.