Since Deng Xiaoping’s economic reforms starting in 1978, the Chinese government has continuously improved the basic laws and regulations that guarantee women’s economic rights and employment rights. Chinese women can participate equally in economic development, and enjoy the fruits of reform and development on an equal footing with men. In China (Aaltio and Huang, 2007), working women now account for 47.0% of the total labor force, higher than the world average of 40.8%. However, in the computing industry, the proportion of female practitioners in China is about 7% (Proginn and Juejin, 2017; Proginn, 2018), significantly lower than 17% in United States (Elizabeth, 2017). The problem of the small proportion of Chinese computing female practitioners should be remedied.

Our modern understanding of institutional identity began with police photography, and the building of Habitual Criminal Registers. These databases participated in building the social ‘archive’, were deployed to prevent recidivism, and developed in the context of evolving interest statistical knowledge systems, as well as biological fatalism in criminology and anthropology. The ‘mechanical objectivity’ of the camera, social, political, and intellectual influences, meant images and the archive were a new way of ‘knowing’ people, especially criminals, deviants, and other undesirables. Shortly after the institutional adoption of photographic registers, other technologies too were needed to make those registers searchable. This provoked the first anthropometrics and biometrics systems, and the first exercises in reducing identity to numerical data.

Algorithmic accountability has emerged as a package of legal ideas that, on one hand, attempt to impose administrative law mechanisms such as transparency and due process on automated decision-making systems, and on the other hand, has developed computational approaches to constraining machine learning. In particular, by ensuring the complex computational analysis of individuals through machine learning models occurs more ‘fairly’, and is more explainable. As well as describing the necessity for computational legal implementations that actively constrain how data processing occurs, the chapter argues that there are risks that these mechanisms may involve ceding to data science and its corporate stakeholders the epistemological terrain as to what types of calculations are ‘fair’ and what type of information is an ‘explanation’.

Latin America and the Caribbean is a vast area reaching two continents, North America and South America, and the islands in and around the Caribbean Sea. This region accounts for 8.6% of the world’s population (UNESCO, 2016). Geographically, Latin America and the Caribbean commences in North America at the United States and Mexico border and terminates in South America at Tierra del Fuego in Chile. The Caribbean includes countries, dependencies, and territories in and around the Caribbean Sea. Latin America and the Caribbean includes thirty-three countries and thirteen dependencies and/or territories. Within Latin America are the geographically recognized larger sub-regions of North America, Central America, and South America. The Caribbean includes nine sub-regions. Within the Caribbean is the Caribbean Community (CARICOM), which is comprised of a grouping of twenty countries, all island states, but does not include all countries within the Caribbean (www.caricom.org).

This chapter focuses on five countries, assessing the economic, cultural, infrastructural, and policy factors influencing women’s ability to enter the IT workforce. The National Assessments on Gender, Science, Technology and Innovation, coordinated by Women in Global Science and Technology (WISAT), is a cross-national research project analyzing country-level data to assess the readiness for and participation of girls and women in a global world defined by knowledge. The assessments look at health, social status, safety and security, economic status, resources, agency, and opportunity and capability dimensions of women’s lives in the context of an enabling policy environment, in order to assess the implications for and outcomes related to women’s participation in knowledge-related sectors, decision-making, and education. Recently, five national studies were undertaken in East and West Africa – Ethiopia, Kenya, Rwanda, Senegal, and Uganda. These studies found the economic, policy, and cultural factors affecting women’s participation in the IT workforce to vary considerably. While there are commonalities, these differences in national context result in different patterns of female participation in the STEM labor force.

In 2012, Alex Krizhevsky, then a PhD student at University of Toronto under Geoffrey Hinton, won the annual ‘ImageNet’ image labelling competition by an impressive 10.8 per cent margin. His use of a neural network-based object classification algorithm would then trigger a major shift the way computers would relate to images and the physical world more generally. ImageNet is an image database first published by computer scientist Fei-Fei Li in 2009 and labelled primarily by Amazon Mechanical Turk workers. Its intention was to ‘map out the entire world of objects’ for the sake of training machine learning systems. The first winner of the ImageNet competition in 2010 achieved a labelling accuracy of 71.8 per cent.

Graduation trends in the last twenty-five years show that majors in computing-related fields have had low popularity among female students in the United States and Europe. For instance, in 2015, US women earned a mere 18% (9,209) of bachelor’s degrees in computer science (CS), which is less than the number earned in 1985 (14,431) (National Science Board, 2018). Similarly, in Europe women represented 16.7% of total graduates in information communication technology (ICT) in 2016 (European Commission, 2018). Low participation of women in computing education has been a pressing problem in Western countries. Gender diversity in computing is imperative as it will increase the skilled labor force pool, enrich innovation, and foster social justice. Most importantly, there is a high demand for people with computing skills. The number of ICT specialists in the European Union grew by 36.1% from 2007 to 2017, more than ten times as high as the increase (3.2%) for total employment (Eurostat, 2018). Employment in computing-related occupations in the United States is projected to grow 13% from 2016 to 2026, which is faster than the average for all occupations. This is expected to add about 557,100 new jobs (US Department of Labor, 2017). Often such growing needs are met by foreign skilled workers, mostly from Asian countries. It is, therefore, no surprise that a number of governmental and corporate initiatives exist in the United States and in Europe to empower students with the computing skills to thrive in a global economy.

Diverse perspectives coming from a diversity of people in the information technology (IT) profession yields benefits both in terms of products and services provided to consumers and in terms of employment opportunities presented to those who would work in this field (Trauth et al., 2006a). In this regard the gender imbalance presents an important challenge to researchers, teachers, and employers. Overcoming the barriers to greater diversity in the field also requires an understanding of the context in which they occur and can be addressed.

Our behaviour in informational environments is governed by new mechanisms of control. Technological environments do not simply enable or constrain specific behaviours, but are instead instrumented so that rational choices of agents are directed towards pre-specified goals. This type of engagement with the informational world is under-theorised in law. This chapter argues that we need to transcend the separation between physical and informational and work on building appropriate techno-legal mechanisms. It suggests we can think about these emerging environments, and the legalities that are implemented into them, as emerging jurisdictions that do not undermine law, but rather give it another form of expression.

In 1882 renowned English scientist Charles Darwin announced that “[t]he chief distinction in the intellectual powers of the two sexes is shewn by man’s attaining to a higher eminence, in whatever he takes up, than can woman” (Darwin, 1871, p. 564). This belief in women’s inferior intellect was not new, but as an eminent scientist, Darwin’s proclamations held great sway in his time and place – and since – although nowadays few would admit to this. Or would they? Jump forward to 1992 and we see the arrival of John Gray’s Men Are from Mars, Women Are from Venus, which became a phenomenal best-seller (selling more than fifteen million copies globally), and continues to be so. While the book is not as forthright in saying women’s intellect is inferior, it does explain the many ways in which men and women differ – including the ways they think (Gray, 1992).

Automated decision-making and profiling are becoming more prolific and changing in nature. What began with police photography and Habitual Criminal Registers has reached a new crescendo with computer vision and data science. Law has struggled to adequately regulate these technologies and practices. Where it has been successful, law constrains profiling by protecting ‘identity’. However, in the contemporary technological environment, the notions of identity that animate the legal thinking and the notions of identity that animate the data science and profiling are markedly different. This chapter introduces the argument that these contradictory ways of thinking about people is why the law has struggled to introduce meaningful regulation in this field. It introduces the metaphor of the ‘world state’, the process by which the world and the people within it are translated into the computational world, and asks what law needs to do as the world state becomes more prominent.