The primary aim of this research is to develop diverse and fair datasets, with a specific focus on marginalized demographics like the Global South and/or non-binary gender groups. I am working towards creating two types of datasets-- (a) Real and Synthetic face datasets from Global South for auditing/training Face Recognition Systems. These are used in tasks like face detection and facial attribute analysis, and (b) Datasets with non-binary gender labels, across modalities like image and text for more inclusive retrieval (visual search enabled e-commerce) and classification (text-based gender prediction) tasks.
Software systems increasingly mediate critical societal functions involving large-scale use of data. The use of personal and sensitive data introduces ethical and legal concerns, necessitating architectures that support Responsible AI and enforce open access safeguards grounded in ethical principles. These principles are conventionally derived through laws, regulations, codes of conduct, and frameworks. Much of the focus in responsible AI has been on privacy alone. However, there can be other sensitive information in the enterprise and public domain that needs to be handled in a responsible and ethical manner as well. Ethical compliance traditionally has been achieved through systematic manual adherence to guidelines, regulations, and other documentation. However, there is a lack of concrete software architectures, frameworks, or tools that can provide automation to enable compliance directly in software systems. This paper introduces Guardrail Framework, a modular, reusable software framework that operationalizes responsible AI principles by decoupling ethical constraints from the functional requirements of software applications. The proposed novel framework addresses the issue of ethical compliance in application software by creating a robust software framework that can be plugged into multiple application software development environments. The framework allows ethical filtering of information on the basis of data sensitivity (Low to High), the trust score of the user (Low to High), and granularity of the data (Cell, Row, Column, or Table). The proposed Guardrail framework is implemented and evaluated using Open Government Dataset (OGD), demonstrating high cohesion, low coupling, and long-term maintainability, in line with fundamental software engineering architectural design principles. Notably, the proposed framework is equally effective for ensuring ethical use of personal, enterprise, and public data.
MIMIC (Medical Information Mart for Intensive Care) is one of the largest, most commonly-used, freely available datasets containing intensive care unit data. I conduct denotative, connotative, and deconstructive readings of the MIMIC-IV dataset through an analysis of the data sources, dataset structure, and the process for getting access to the data, as well as documents and concepts related to the dataset. As a result, I demonstrate that the MIMIC-IV dataset requires more documentation, including an expansion of the existing descriptions, in order to ensure the data is used appropriately and allow for maximum benefit. I make recommendations for future users of the MIMIC-IV dataset, creators of datasets in general, and researchers in the Critical Data Studies field based on my findings.
This paper examines the dilemmas of moral autonomy in the context of the transfer of human consciousness to AI, focusing on the transhumanist archetype Homo Evolutus. Through comparative analysis, the study explores two distinct paradigms: the secular approach, rooted in Kantian philosophy, which considers that moral autonomy can be preserved through transfer; and the religious perspective (Catholic, Protestant, and Orthodox), which argues that the human person is an essential unity of body and soul, making the transfer conceptually impossible. The study highlights three central dilemmas: the authenticity of digital moral reflection, the distribution of responsibility between the biological and digital selves, and the limits of autonomy in human-AI integration. The secular paradigm perpetuates Cartesian dualism, while the theological perspective asserts that consciousness transfer raises fundamental ontological problems that cannot be solved by secular approaches alone. The conclusion emphasizes the need for an interdisciplinary approach that includes theological aspects to clarify what it means to be a moral agent in a technologically altered world.
This thesis examines how AI systems deployed in Malaysia’s smart cities encode constitutional and racial asymmetries through algorithmic infrastructures. Focusing on epistemic governance, it analyses how data, models, and legal frameworks shape machine legibility and citizen classification, with comparative insights from India and Brazil to contextualise Malaysia’s postcolonial governance model.
