This chapter presents an interview with Vint Cerf, one of the fathers of the Internet. The chapter discusses his personal views on ethics, data and privacy, net neutrality, public policy, self-driving cars, genetic codes, and reflections on the future.

The global engineering/construction industry is huge. In 2017, it was estimated to be an $8.8 trillion industry (Market Research Hub, 2016). The US construction industry in 2017 was estimated at $1.2 trillion (Wilcox, 2018). Because the industry is comprised of a myriad of projects to build new facilities or to repair or upgrade existing ones, it is often the location for bribery, fraud, and corruption. Government leaders in Panama, Brazil, and Spain have been removed from office for receiving bribes and kickbacks from projects in their countries. Engineering firms in the United States and Canada have been sanctioned for giving bribes to secure projects. These are the facts.

In an era of corporate mistrust, creating sustainable ethical corporations goes beyond implementing governance, risk, and compliance (GRC) strategy. It requires an ongoing intensified spotlight to make the highest ethical standards the norm, and ruthless intolerance of anything less. Corporations are at a tipping point seeking to build sustainable businesses while striving to avoid a front-page scandal. They are placing greater scrutiny on values as business enabler, leadership accountability, and building ethical decision-making as an integrated business process. The next generation of ethical systems is at our corporate doorstep. As Albert Einstein famously said, “we cannot solve problems by using the same kind of thinking we used when we created them.” Today’s workplace has an unprecedented four generations working alongside each other. Globalization and the flattened twenty-first-century economy have pivotally shifted the norms of communication, information sharing, and collaboration. Greater visibility through mass media and social media has revealed new consumer and corporate behaviors. With greater transparency at our fingertips, trust has become the new currency, evidenced in the backlash as trust in public officials and corporate leaders steadily declines. The Edelman Trust Barometer has been studying trust across four institutions since 2012: businesses, government, nongovernmental organizations (NGOs), and media. Their 2017 report reveals that trust has declined broadly across all four institutions and that trust is in crisis around the world.

Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and well-being. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and for society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation.

Medicine has a dichotomous personality, some of it is science and some of it is art. The science of medicine focuses on the technical skills and proficiency; whereas the art of medicine examines the ethical decision-making, professionalism, and relationships we foster to provide care to patients, comfort to families, and compassion to colleagues. It is often referred to as bedside manner, but it extends beyond that. It is communication, honesty, and respect.

There is an emerging body of legal thought directed at contemporary profiling and data science. Some of this focuses on limiting ‘human computability’, some addresses questions of ‘manipulation’ and ‘behavioural optimisation’, and some suggests ways to introduce friction into the information environment to interrupt the translation of data into meaning. This chapter looks at how some of these ideas might be implemented as computational legal applications. It argues that the legal subject of algorithmic accountability can be expanded into a rights-bearing entity that can actively contest how it is computationally interpreted, through mechanisms of ‘contestation by design’. The chapter also describes the utility of concepts like ‘context’ for building boundaries and friction into information architectures, not simply in terms of information flow but also for constraining how the design of those architectures influences and structures behaviour. Finally, it suggests the shape of a new ‘composite’ legal person as a mechanism to constrain profiling behavior by producing an identity as an interface to the ‘world state’ it inhabits.

It has been twelve years since our article “Women in Computer Science: No Shortage Here!” (Othman and Latih, 2006) was published. It is disheartening that after more than a decade, gender disparity in computer science (CS) is still an issue. Among important findings of our previous study is that young Malaysian females and males have a markedly different attitude toward science and mathematics compared with their Western counterparts. CS and information technology (IT) is not viewed as a masculine field by young Malaysians, which is a key reason why this nation does not encounter the problem of too few females being interested in pursuing a degree in CS/IT.

Historically, it is known that women had an important role in computing. History lessons on computer science narrate that women were some of the first software engineers until technology and practices changed the role of women as programmers.

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.