Classification
AI Model Architectures and Algorithms
Overview
Support Vector Machine (SVM) is a supervised learning algorithm widely used for classification tasks, while Support Vector Regression (SVR) extends the SVM framework to regression problems for predicting continuous values. Both techniques rely on finding the hyperplane that best separates data points (for SVM) or fits the data within a specified margin (for SVR) in a high-dimensional space. They are particularly effective in structured data scenarios and can handle non-linear relationships through the use of kernel functions. SVM/SVRs are robust to overfitting in high-dimensional spaces when regularization is properly applied, and they can be adapted for multi-class tasks through strategies like one-vs-rest. However, SVM/SVRs can be computationally intensive with large datasets and may require careful parameter tuning (e.g., kernel choice, regularization) to avoid overfitting or underfitting. Additionally, interpretability is limited compared to linear models, which can pose challenges in regulated sectors.
Governance Context
AI governance frameworks, such as the EU AI Act and NIST AI Risk Management Framework, require organizations to ensure model transparency, robustness, and fairness. For SVM/SVR, this means documenting model selection rationale, hyperparameter choices, and kernel functions used, as well as performing bias and fairness assessments on the training data and outputs. Obligations may include providing explanations for automated decisions (as per GDPR Article 22) and conducting regular audits to detect shifts in data distributions that could impact model performance or fairness. Controls such as reproducibility documentation and thorough validation procedures are recommended to satisfy compliance and risk management requirements. Concrete obligations include: 1) maintaining detailed records of model development, parameter settings, and decision logic to facilitate transparency and auditability, and 2) implementing periodic bias and fairness audits to ensure compliance with non-discrimination requirements.
Ethical & Societal Implications
SVM/SVR models can inadvertently perpetuate biases present in training data, leading to unfair or discriminatory outcomes in sensitive applications like healthcare or lending. The black-box nature of kernelized SVMs complicates transparency and accountability, making it difficult for affected individuals to understand or contest automated decisions. Additionally, overreliance on these models without rigorous validation can result in societal harms, such as exclusion or misdiagnosis, particularly for minority or marginalized groups. There is also a risk that model complexity or lack of documentation can hinder effective governance and redress for impacted individuals.
Key Takeaways
SVM handles classification; SVR is for regression with continuous outputs.; Kernel choice and parameter tuning critically affect performance and fairness.; Transparency and explainability are limited, especially in complex kernelized models.; Regular audits and bias assessments are essential for responsible deployment.; Compliance with frameworks (GDPR, EU AI Act, NIST) requires documentation and monitoring.; SVM/SVRs are effective for structured data but may not scale well to large datasets.; Governance controls must address both technical performance and ethical impact.