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This article reviews the political and ethical dimensions of technology applications in social work by focusing on a descriptive case study. The case study is of an initiative undertaken by the New Zealand government between 2011 and 2015 to develop an algorithm that would allow child protection services to predict future child maltreatment at the point of birth and to pre-empt it before it occurs. The paper explores the new threats to human rights and social justice associated with the rise of algorithmic governance and explores the sources of algorithmic injustice. It also outlines some of the key ethical issues and political challenges associated with algorithmic governance.

This chapter highlights evidence that links women's health challenges in LMICs to economics at every stage of life. It advances the notion that discrimination against women persists for sociocultural and economic reasons and is embodied in ill health and disability across the life span.

Beginning in infancy and early childhood, girls face a variety of disadvantages that profoundly affect their health and well-being. These barriers — which include poor access to health care, nutrition and education — continue to affect women, their health, and their economic viability across the life cycle. In adolescence girls face risks associated with gender-based violence and sexually transmitted diseases, while early marriage and early pregnancy contribute to their exit from schooling. Adulthood for women is then marked by a myriad of interrelated issues — including reproductive health concerns, chronic disease, obesity, injury, intimate partner violence (IPV) and mental health problems — which encompass the long-term impact of developmental adversities. Finally, in old age, women's burdens of disease and disability reflect the health inequities faced throughout life and often coincide with a lack of financial security, neglect and abuse. Effectively addressing these challenges requires a comprehensive approach, including efforts to target gender-based inequities, educational campaigns and diagonal health systems strategies.