Threatcasting is a foresight methodology that examines the worst of potential future changes by imagining and crafting a fictional (but very plausible) story of a person, in a detailed setting, experiencing a threat. In this dissertation, I investigate the processes and techniques of threatcasting, focused primarily on the post-analysis phase, and demonstrate it as an open methodology that can embrace varied ways to analyze raw data and seek conclusions. I incorporate best practices of narrative and thematic analysis, qualitative analysis, grounded theory, and hypothesis-driven theories of inquiry. I use interviews from futurists trained on threatcasting ways of thinking and compare two case studies - one using a grounded theory approach on the future of weapons of mass destruction and cyberspace and the other using a hypothesis-driven approach on the future of extremism - to investigate the efficacy of different theoretical approaches to analysis. I introduce definitions of novelty and ways to assess how a novel finding may have more impact on the future than it appears at first glance. Often, this impact comes more from what is not present in threat scenarios than what is included. Finally, I illustrate how threatcasting, as a practice, is a valuable contribution to those in a position to be responsible architects of a better future.

In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput biological sequencing methodologies providing snapshots of an organism’s molecular profile, biological data is rapidly increasing in its prevalence. As more biological data continues to be harvested, artificial intelligence and machine learning are well positioned to aid in leveraging this big data for breakthrough scientific outcomes and revolutionized medical care. <br/><br/>The coming decade’s intersection between biology and computational science will be ripe with opportunities to utilize biological big data to advance human health and mitigate disease. Standardization, aggregation and centralization of this biological data will be critical to drawing novel scientific insights that will lead to a more robust understanding of disease etiology and therapeutic avenues. Future development of cheaper, more accessible molecular sensing technology, in conjunction with the emergence of more precise wearables, will pave the road to a truly personalized and preventative healthcare system. However, with these vast opportunities come significant threats. As biological big data advances, privacy and security concerns may hinder society's adoption of these technologies and subsequently dampen the positive impacts this information can have on society. Moreover, the openness of biological data serves as a national security threat given that this data can be used to identify medical vulnerabilities in a population, highlighting the dual-use implications of biological big data. <br/><br/>Additional factors to be considered by academia, private industry, and defense include the ongoing relationship between science and society at-large, as well as the political and social dimensions surrounding the public’s trust in science. Organizations that seek to contribute to the future of biological big data must also remain vigilant to equity, representation and bias in their data sets and data processing techniques. Finally, the positive impacts of biological big data lie on the foundation of responsible innovation, as these emerging technologies do not operate in standalone fashion but rather form a complex ecosystem.