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Big data have rapidly developed as a viable solution to many problems faced in engineering industries. Specifically, in the industry of water resource engineering, where there is a tremendous amount of data, various big data techniques could be applied to achieve innovative and efficient solutions for the industry. This study reviewed the proposal of big data as potential approaches to solve various difficulties encountered in managing water resources and related applications in Malaysia. The advantages and disadvantages of big data applications have also been discussed along with a brief literature review and some examples of case studies.

The nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.

When immigrants experience “nationality discrimination” in the labor market, ceteris paribus their earnings are lower than native-born workers because they were born abroad. The challenge to testing for nationality discrimination is that the native/immigrant earnings gap will very likely also be influenced by productivity differences driven by incomplete assimilation of immigrants, as well as the possibility of racial or gender discrimination. There is relatively little empirical literature, and virtually no theoretical literature, on this type of discrimination. In this study, a model of nationality discrimination where customer prejudice and native/immigrant productivity differences jointly influence the earnings gap is presented. We derive an extension of Becker's market discrimination coefficient (MDC), applied to the case of nationality discrimination when there are productivity differences. A number of novel implications are obtained. We find, for example, that the MDC depends upon relative immigrant productivity and relative immigrant labor supply. We test the model on data for hitters and pitchers in Major League Baseball, an industry with a history of immigration, potential for customer discrimination, and clean detailed microdata on worker productivities and race. Ordinary least squares (OLS) and decomposition methods are used to estimate the extent of discrimination. We find no compelling evidence of discrimination in the hitter group, but evidence of ceteris paribus underpayment of immigrant pitchers. While our test case is for a particular industry, our theoretical model, empirical specifications, and general research design are quite generalizable to many other labor markets.