We compare how in-kind food assistance and an electronic voucher-based program affect the delivery of aid in practice. The Government of Indonesia randomized across 105 districts the transition from in-kind rice to approximately equivalent electronic vouchers redeemable for rice and eggs at a network of private agents. Targeted households received 46 percent more assistance in voucher areas. For the bottom 15 percent of households at baseline, poverty fell 20 percent. Voucher recipients received higher-quality rice, and increased consumption of eggs. The results suggest moving from a manual in-kind to electronic voucher-based program reduced poverty through increased adherence to program design.

Women in Congress are highly effective legislators. Yet, if women are more likely than men to be interrupted during committee work, they may face a gender-related impediment. We examine speech patterns during more than 24,000 congressional committee hearings from 1994 to 2018 to determine whether women Members are more likely to be interrupted than men. We find that they are. This is especially true in Senate committees—where women are about 10% more likely to be interrupted. Furthermore, in hearings that discuss women’s issues, women are more than twice as likely to be interrupted than while discussing other issues. We see a similar pattern for rapid-fire “interruption clusters,” an aggressive form of interruption. We further consider a range of moderating factors, which yields little evidence that women change their communication strategy as they gain experience in Congress. We also find suggestive evidence that interruptions are driven by mixed-gender interactions.

How do parents decide what goods, experiences, and activities they can afford for their children during times of economic insecurity? This article draws on 72 in-depth interviews with U.S. professional middle-class families in which one parent is unemployed. Extending the concept of relational work, this study illuminates how the microfoundation of economic decisions is gendered. Families where fathers are unemployed take the approach of relational preservation: they seek to maintain a high threshold of expenditures on children and view curtailing child-related spending as a threat to their class status. These families see reducing expenditures on children as a parental, and especially paternal, failure. Families where mothers are unemployed take an approach of relational downscaling, lowering the threshold for essential expenditures on children. These families are reluctant to spend less on children’s education, but they do not view decreasing spending on other items, such as consumer goods, as threatening their class status. Gendering relational work reveals how inequalities within families are reproduced through meaning-making around expenditures on children, and it clarifies a key source of variation in parental economic decision-making.

The Population-based HIV Impact Assessment (PHIA) is an ongoing project that conducts nationally representative HIV-focused surveys for measuring national and regional progress toward UNAIDS’ 90-90-90 targets, the primary strategy to end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to better understand the key factors that drive the HIV epidemics in the most affected countries in sub-Saharan Africa. In this article, we propose a novel causal structural learning algorithm to discover important covariates and potential causal pathways for 90-90-90 targets. Existing constraint-based causal structural learning algorithms are quite aggressive in edge removal. The proposed algorithm preserves more information about important features and potential causal pathways. It is applied to the Malawi PHIA (MPHIA) dataset and leads to interesting results. For example, it discovers age and condom usage to be important for female HIV awareness; the number of sexual partners to be important for male HIV awareness; and knowing the travel time to HIV care facilities leads to a higher chance of being treated for both females and males. We further compare and validate the proposed algorithm using BIC and using Monte Carlo simulations, and show that the proposed algorithm achieves improvement in true positive rates in important feature discovery over existing algorithms. Supplementary materials for this article are available online.

On the ground, how can research initiatives unfold to make meaningful contributions to real-world practice and real-time policy? This article draws on a case study evaluating an innovative program to alleviate toxic stress, boost resilience, and promote social inclusion among Syrian refugee and Jordanian nonrefugee youth. I describe the kind of project design and community engagement that animates research on stress biology and lived experiences, connecting people with humanitarian practice and policy. I highlight why and how biocultural work generates fluency in multiple forms of evidence to guide mental health interventions, reflecting on ways to anchor research in shared humanity and shared scientific purpose. I clarify what types of added value, pursued during intersectoral collaborations, help achieve plural, sustained, and inclusive contributions. This article shows how “creative relationality” can energize research-to-policy initiatives to bring about transformational change.

Through a study of the Indian Central Relief Committee for Tibetans and the American Emergency Committee for Tibetan Refugees, this article maps the multiple dimensions of Indian and American civil society advocacy on behalf of Tibet in the immediate aftermaths of the Dalai Lama’s 1959 flight to India: anticommunism, imperialism, discourses of religious freedom and civilizational solidarity, domestic politics, and regional security interests. These contexts did not operate separately, but rather formed layered interactions, layers that eventually bound Tibetan autonomy. While the Dalai Lama and other Tibetan nationalists worked across the geographic and political spectrum to generate international support as a matter of practicality and necessity, the complex web from which this support came, and through which it operated, functioned as constraints as well as backing. Advocacy from such a disparate set of national, personal, religious, and political interests came with limitations that defined Tibetans as communist victims, an oppressed religious minority, and a humanitarian commodity, but not as nationalist claimants.

Researchers, governments, ethics watchdogs, and the public are increasingly voicing concerns about unfairness and bias in artificial intelligence (AI)-based decision tools. Psychology’s more-than-a-century of research on the measurement of psychological traits and the prediction of human behavior can benefit such conversations, yet psychological researchers often find themselves excluded due to mismatches in terminology, values, and goals across disciplines. In the present paper, we begin to build a shared interdisciplinary understanding of AI fairness and bias by first presenting three major lenses, which vary in focus and prototypicality by discipline, from which to consider relevant issues: (a) individual attitudes, (b) legality, ethicality, and morality, and (c) embedded meanings within technical domains. Using these lenses, we next present psychological audits as a standardized approach for evaluating the fairness and bias of AI systems that make predictions about humans across disciplinary perspectives. We present 12 crucial components to audits across three categories: (a) components related to AI models in terms of their source data, design, development, features, processes, and outputs, (b) components related to how information about models and their applications are presented, discussed, and understood from the perspectives of those employing the algorithm, those affected by decisions made using its predictions, and third-party observers, and (c) meta-components that must be considered across all other auditing components, including cultural context, respect for persons, and the integrity of individual research designs used to support all model developer claims.