From the 1960s onwards, many Iranians closely followed Black American protests during the Civil Rights and Black Power movements in the United States. This period proved pivotal for Iranian understandings of race, where intellectuals, revolutionaries, and those in media would use US-centric histories of enslavement, racism, and Black Americans to erase nineteenth-century histories of enslavement and racism in Iran, tacitly displacing the existence of Black Iranians across the national landscape. Black American Muslims, particularly Malcolm X, emerged as the ideal form of Blackness. After the 1979 revolution, non-Black Iranians and the Iranian government would continue this focus on US-based racism through an official narrative that repeatedly defined racism as a US-only problem, ultimately cementing the erasures around histories of enslavement and Black Iranians that began with abolition in 1929. Through an analysis of speeches, memoirs, poetry, newspaper articles, photography, and other illustrated media, this article weaves together vignettes to demonstrate how the pervasiveness of racial hierarchies fashioned around US histories came to shift an Iranian vocabulary and conceptualization of race. This article traces the changes in racial discourse during the 1960s and 1970s, the 1979 revolution, and the Iran-Iraq War from an Iranian perspective.

Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities.