Empowering women facing gender-based violence amid COVID-19 through media campaigns.

COVID-19 heightened women's exposure to gender-based and intimate partner violence, especially in low-income and middle-income countries. We tested whether edutainment interventions shown to successfully combat gender-based and intimate partner violence when delivered in person can be effectively delivered using social (WhatsApp and Facebook) and traditional (TV) media. To do so, we randomized the mode of implementation of an intervention conducted by an Egyptian women's rights organization seeking to support women amid COVID-19 social distancing. We found WhatsApp to be more effective in delivering the intervention than Facebook but no credible evidence of differences across outcomes between social media and TV dissemination. Our findings show little credible evidence that these campaigns affected women's attitudes towards gender or marital equality or on the justifiability of violence. However, the campaign did increase women's knowledge, hypothetical use and reported use of available resources.

Evidence on the nature of sectarian animosity from a geographically representative survey of Iraqi and Iranian Shia pilgrims.

Sectarian tensions underlie conflicts across the Middle East, but little is known about their roots and associated beliefs. We conducted a large-scale empirical analysis, drawing on an original, geographically representative survey of over 4,000 devout Shiites across Iran and Iraq. We find that sectarian animosity is linked to economic deprivation, political disillusionment, lack of out-group contact and a sect-based view of domestic politics-paralleling patterns seen in ethno-nationalism elsewhere. In contrast, two alternative accounts are largely unsupported: sectarian animosity is not consistently associated with solidarity with a transnational sect-based community, nor does it seem to stem from disputes over religious doctrine. Nonetheless, this identity's religious roots manifest in differences from typical ethno-nationalism; practising men are less sectarian, consistent with official doctrine encouraging unity, whereas practising women are more sectarian. These gendered patterns suggest an understudied mechanism: religiously mediated socialization, or the transmission of non-religious norms through religious practice.

Mitigating the impact of biased artificial intelligence in emergency decision-making.

BACKGROUND: Prior research has shown that artificial intelligence (AI) systems often encode biases against minority subgroups. However, little work has focused on ways to mitigate the harm discriminatory algorithms can cause in high-stakes settings such as medicine. METHODS: In this study, we experimentally evaluated the impact biased AI recommendations have on emergency decisions, where participants respond to mental health crises by calling for either medical or police assistance. We recruited 438 clinicians and 516 non-experts to participate in our web-based experiment. We evaluated participant decision-making with and without advice from biased and unbiased AI systems. We also varied the style of the AI advice, framing it either as prescriptive recommendations or descriptive flags. RESULTS: Participant decisions are unbiased without AI advice. However, both clinicians and non-experts are influenced by prescriptive recommendations from a biased algorithm, choosing police help more often in emergencies involving African-American or Muslim men. Crucially, using descriptive flags rather than prescriptive recommendations allows respondents to retain their original, unbiased decision-making. CONCLUSIONS: Our work demonstrates the practical danger of using biased models in health contexts, and suggests that appropriately framing decision support can mitigate the effects of AI bias. These findings must be carefully considered in the many real-world clinical scenarios where inaccurate or biased models may be used to inform important decisions.

Artificial intelligence (AI) systems that make decisions based on historical data are increasingly common in health care settings. However, many AI models exhibit problematic biases, as data often reflect human prejudices against minority groups. In this study, we used a web-based experiment to evaluate the impact biased models can have when used to inform human decisions. We found that though participants were not inherently biased, they were strongly influenced by advice from a biased model if it was offered prescriptively (i.e., "you should do X"). This adherence led their decisions to be biased against African-American and Muslims individuals. However, framing the same advice descriptively (i.e., without recommending a specific action) allowed participants to remain fair. These results demonstrate that though discriminatory AI can lead to poor outcomes for minority groups, appropriately framing advice can help mitigate its effects.

Community policing does not build citizen trust in police or reduce crime in the Global South.

Is it possible to reduce crime without exacerbating adversarial relationships between police and citizens? Community policing is a celebrated reform with that aim, which is now adopted on six continents. However, the evidence base is limited, studying reform components in isolation in a limited set of countries, and remaining largely silent on citizen-police trust. We designed six field experiments with Global South police agencies to study locally designed models of community policing using coordinated measures of crime and the attitudes and behaviors of citizens and police. In a preregistered meta-analysis, we found that these interventions led to mixed implementation, largely failed to improve citizen-police relations, and did not reduce crime. Societies may need to implement structural changes first for incremental police reforms such as community policing to succeed.

Context modularity of human altruism.

Whereas altruism drives the evolution of human cooperation, ethno-religious diversity has been considered to obstruct it, leading to poverty, corruption, and war. We argue that current research has failed to properly account for the institutional environment and how it affects the role diversity plays. The emergence of thriving, diverse communities throughout human history suggests that diversity does not always lead to cooperation breakdown. We conducted experiments in Mostar, Bosnia-Herzegovina with Catholic Croats and Muslim Bosniaks at a critical historic moment in the city's postwar history. Using a public goods game, we found that the ability to sanction is key to achieving cooperation in ethno-religiously diverse groups, but that sanctions succeed only in integrated institutional environments and fail in segregated ones. Hence, we show experimentally for the first time in a real-life setting that institutions of integration can unleash human altruism and restore cooperation in the presence of diversity.