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Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Despite increasingly persuasive women-focused marketing of gambling products, there has only been limited investigation around women sports betting. Men remain the focus of much of the conversation about sports betting as they have generally been found to be the most active sports bettors and the most at risk of experiencing harms associated with their behaviour. This chapter aims to fill this gap by exploring the characteristics of young women sports bettors in the United Kingdom and the relationship between sports betting and the experience of gambling harms. To do this, we created two models of analysis. Our analysis is based on data from the first wave (2019) of the Emerging Adults Gambling Survey (EAGS) dataset (n = 3,549). The EAGS is a non-probability longitudinal survey that includes individuals between the ages of 16 and 24 who were residents in Britain at the time of data collection. Firstly, we examined the associations between women sports bettors and several factors identified as important predictors of sports betting. Secondly, we sought to understand the relationship between women's sports betting and the harms associated with this activity. From these models, we found that women's sports betting was most reliably predicted according to fandom and peer influence. We also found that women sports bettors were more at risk of experiencing harms associated with difficulties with family and friends than women gamblers using other products.

While research on digital dangers has been growing, studies on their respective solutions and justice responses have not kept pace. The agathokakological nature of technology demands that we pay attention to not only harms associated with interconnectivity, but also the potential for technology to counter offenses and “do good.” This chapter discusses technology as both a weapon and a shield when it comes to violence against women and girls in public spaces and private places. First, we review the complex and varied manifestations of technological gender violence, ranging from the use of technology to exploit, harass, stalk, and otherwise harm women and girls in communal spaces, to offenses that occur behind closed doors. Second, we discuss justice-related responses, underscoring how women and girls have “flipped the script” when their needs are not met. By developing innovative ways to respond to the wrongs committed against them and creating alternate systems that offer a voice, victims/survivors have repurposed technology to redress harms and unite in solidarity with others in an ongoing quest for justice.