LSE MSc Artificial Intelligence Governance Supplemental Essay Example: Same Field Deepening (Score 92)

My quantitative preparation is anchored in a computer science curriculum at the University of the Philippines, where I developed proficiency in statistical inference, linear algebra, and machine learning. I have executed supervised and unsupervised learning models in Python (scikit-learn, TensorFlow), applying regression, classification, and clustering to real-world datasets. In my AI Governance Applied Project, I built a risk-scoring model for algorithmic decision-making in government procurement, integrating logistic regression and decision trees to evaluate bias and fairness metrics. I am also comfortable with data visualisation in R and Tableau and have used network analysis to map stakeholder influence within AI policy ecosystems. Despite this foundation, I recognise specific gaps as I transition from technical implementation to governance analysis. While confident executing machine learning pipelines and standard statistical tests, I have limited experience with advanced econometric techniques and causal inference frameworks central to AI policy evaluation. My exposure to Bayesian methods remains introductory, and I have not yet worked with large-scale administrative datasets or conducted formal impact evaluations. To address these gaps, I have enrolled in a pre-sessional online course on causal inference covering difference-in-differences and propensity score matching, and I plan to complete an advanced quantitative policy analysis module before enrolment. My applied outputs demonstrate the ability to translate quantitative methods into actionable governance insights. I authored a technical appendix for a workplace policy memo submitted to the Department of Information and Communications Technology in Manila, detailing model validation steps and sensitivity analyses for bias detection in automated systems. I also produced an analyst note evaluating algorithmic transparency in a Metro Manila e-governance pilot, with reproducible code and full data documentation. Both outputs reflect a commitment to methodological rigour and policy relevance that I intend to carry into postgraduate research. If the committee requests further evidence, I can provide annotated code repositories, sample data visualisations, excerpts from applied project reports, and supervisor references attesting to the independence and rigour of my quantitative work in both academic and professional settings.