Algorithmic Regulation using AI and Blockchain Technology

This thesis investigates the application of AI and blockchain technology to the domain of Algorithmic Regulation. Algorithmic Regulation refers to the use of intelligent systems for the enabling and enforcement of regulation (often referred to as RegTech in financial services). The research work focuses on three problems: a) Machine interpretability of regulation; b) Regulatory reporting of data; and c) Federated analytics with data compliance. Uniquely, this research was designed, implemented, tested and deployed in collaboration with the Financial Conduct Authority (FCA), Santander, RegulAItion and part funded by the InnovateUK RegNet project. I am a co-founder of RegulAItion. / Using AI to Automate the Regulatory Handbook: In this investigation we propose the use of reasoning systems for encoding financial regulation as machine readable and executable rules. We argue that our rules-based “white-box” approach is needed, as opposed to a “black-box” machine learning approach, as regulators need explainability and outline the theoretical foundation needed to encode regulation from the FCA Handbook into machine readable semantics. We then present the design and implementation of a production-grade regulatory reasoning system built on top of the Java Expert System Shell (JESS) and use it to encode a subset of regulation (consumer credit regulation) from the FCA Handbook. We then perform an empirical evaluation, with the regulator, of the system based on its performance and accuracy in handling 600 “real- world” queries and compare it with its human equivalent. The findings suggest that the proposed approach of using reasoning systems not only provides quicker responses, but also more accurate results to answers from queries that are explainable. / SmartReg: Using Blockchain for Regulatory Reporting: In this investigation we explore the use of distributed ledgers for real-time reporting of data for compliance between firms and regulators. Regulators and firms recognise the growing burden and complexity of regulatory reporting resulting from the lack of data standardisation, increasing complexity of regulation and the lack of machine executable rules. The investigation presents a) the design and implementation of a permissioned Quorum-Ethereum based regulatory reporting network that makes use of an off-chain reporting service to execute machine readable rules on banks’ data through smart contracts b) a means for cross border regulators to share reporting data with each other that can be used to given them a true global view of systemic risk c) a means to carry out regulatory reporting using a novel pull-based approach where the regulator is able to directly “pull” relevant data out of the banks’ environments in an ad-hoc basis- enabling regulators to become more active when addressing risk. We validate the approach and implementation of our system through a pilot use case with a bank and regulator. The outputs of this investigation have informed the Digital Regulatory Reporting initiative- an FCA and UK Government led project to improve regulatory reporting in the financial services. / RegNet: Using Federated Learning and Blockchain for Privacy Preserving Data Access In this investigation we explore the use of Federated Machine Learning and Trusted data access for analytics. With the development of stricter Data Regulation (e.g. GDPR) it is increasingly difficult to share data for collective analytics in a compliant manner. We argue that for data compliance, data does not need to be shared but rather, trusted data access is needed. The investigation presents a) the design and implementation of RegNet- an infrastructure for trusted data access in a secure and privacy preserving manner for a singular algorithmic purpose, where the algorithms (such as Federated Learning) are orchestrated to run within the infrastructure of data owners b) A taxonomy for Federated Learning c) The tokenization and orchestration of Federated Learning through smart contracts for auditable governance. We validate our approach and the infrastructure (RegNet) through a real world use case, involving a number of banks, that makes use of Federated Learning with Epsilon-Differential Privacy for improving the performance of an Anti-Money-Laundering classification model

A God Worth Worshiping: Toward a Critical Race Theology

Theologian James Cone has declared that White supremacy is the American Church’s greatest, original, and most persistent sin. Although the Church has engaged in numerous attempts to remedy racism, theology still seems to witness to a God that stands relatively unopposed to the status quo of racial injustice and marginalization. This dissertation begins with the claim that Christian theology still operates from the normativity of whiteness. I will argue that, although the Church has made admirable progress with regard to racial justice, the attempts have been at the surface: the underlying structural logic of White supremacy remains intact. My thesis will be that the systemic problem in North American Christianity of a persistent “White privileged theology” or “normalized whiteness” can best be eliminated by constructing a theological response in classical categories—theodicy, anthropology, and epistemology. A black existential phenomenological approach—perhaps best illustrated in the work of Lewis R. Gordon—and the intersectional analysis exemplified in critical race studies, offer significant promise for exposing and correcting the existing methodological privilege of White theology. First, this project begins with the challenge presented in William. R. Jones’ Is God a White Racist? A Preamble to Black Theology. In this provocative work, Jones contends that Black suffering vis-à-vis God’s divine justice should form the methodological core of any theology that purports to support economic, social, and political justice. In taking up Jones’ challenge, I argue that the theodical question can be foregrounded without the humanist conclusions that Jones draws. Second, I look at the anthropological question of who counts as human before God. Focusing on how scholars of color have always prioritized an anthropological structuring of humanity that does not traffic in universal notions of value, I argue for a theological anthropology that specifically opposes the dehumanizing axiological systems that denigrate those who deviate from the normativity of whiteness. Thirdly, this dissertation looks at epistemology as a framing device for how some people are seen, mis-seen, or not seen at all. In particular, I evaluate how the Church has been complicit in the rendering of some as invisible or less worthy of concern

On Prospective Technology Studies

This volume includes papers to technological foresight, roadmapping and TA from two sources. On the one side it is based on a workshop in Budapest at the end of 2007, that was organized in the framework of the International Forum on Sustainable Technological Development. On the other side selected presentations from the symposium on History of Prospective Technology Studies, in the framework of the XXIII International Congress of History of Science and Technology, Budapest, July 2009