Over the years, the field of forensic anthropology has become more diverse, bringing unique perspectives to a previously homogeneous field. This diversification has been accompanied by recognizing the need for advocacy and activism in an effort to support the communities we serve: marginalized communities that are often overrepresented in the forensic population. As such, forensic anthropologists see the downstream effects of colonialism, white supremacy, inequitable policies, racism, poverty, homophobia, transphobia, gun violence, and misogyny. Some argue that advocacy and activism have no place in forensic anthropological praxis. The counterarguments for engaging in advocacy and activism uphold white, heterosexual, cisgender, and ableist privilege by arguing that perceived objectivity and unbiased perspectives are more important than personally biasing experiences and positionality that supposedly jeopardize the science and expert testimony. Advocacy and activism, however, are not new to the practice of anthropology. Whether through sociocultural anthropology, archaeology, or other areas of biological anthropology, activism and advocacy play an important role, using both the scientific method and community engagement. Using a North American approach, we detail the scope of the issues, address how advocacy and activism are perceived in the wider discipline of anthropology, and define ways in which advocacy and activism can be utilized more broadly in the areas of casework, research, and education.

Applications of machine learning (ML) models in forensic anthropology have increased in the last half decade. It is, therefore, important to understand the context in which machine learning models are being used in this discipline. The aim of this paper is to provide the current state of machine learning applications in forensic anthropology through a systematic review process of the literature. This paper provides a descriptive summary of existing literature, rather than a deep critical analysis of the methodological robustness. The literature search was performed using Scopus and Web of Science from 1987 to 2024. Eligible studies were investigated if they had a forensic anthropological focus with an application of machine learning. A total of 167 papers were analyzed after the exclusion criteria were applied. The results of this paper demonstrate that there is a wide range of machine learning model applications in forensic anthropology, utilizing diverse bones, applied to all aspects of the biological profile and some aspects of trauma analysis. Through this review, it is also encouraged to use ML models on underutilized skeletal elements to optimize the pattern recognition capabilities of machine learning for validation of forensic anthropological assessments. Validating ML models on less commonly analyzed skeletal elements will increase the skeletal elements that can be utilized to assist in identifying and repatriating individuals. The review also demonstrates that there is an increased need to provide a comprehensive description of the machine learning applications to increase transparency, interpretability, and further validation in forensic anthropological assessments.

The use of machine learning (ML) models in forensic anthropology (FA) has increased in the last half decade; however, there is a lack of a standardized protocol on how to curate, use, and assess ML models. We introduce PUMAA (A Protocol for Utilizing Machine Learning in Forensic Anthropological Analyses), which includes a flowchart and a checklist that forensic practitioners can follow when creating, using, or assessing ML models for forensic research. The five factors that must be assessed in determining an ML model's true performance are explored. The most common types of supervised ML models are also explained in lay terms and accompanied by visuals to increase accessibility of these complex concepts. Although not exhaustive, examples of information that should be reported have been discussed for seven types of ML models. In addition, their various strengths and limitations are evaluated in order to equip researchers with the necessary tools to make decisions regarding when and how to use ML models. This protocol provides an initial standard for the use of ML in FA.