Causal-Driven Interpretation and Inspection of Deep Learning

PhD Thesis Proposal Defence


Title: "Causal-Driven Interpretation and Inspection of Deep Learning"

by

Mr. Zhenlan JI


Abstract:

Deep learning systems have achieved remarkable success across various 
domains, yet their black-box nature and inherent complexity present 
significant challenges to interpretability, reliability, and software 
quality assurance. To address these challenges, this thesis leverages 
causality, a canonical analysis framework for understanding complex systems, 
to enhance the interpretability and inspection of deep learning systems. 
This thesis establishes causality-based methodologies that offer a 
principled and scalable foundation for the interpretation and analysis of 
deep learning systems across various aspects and granularity levels, from 
neuron-level interactions to system-level evaluations.

Specifically, this thesis contains three key contributions. First, it 
introduces Causal Coverage (CC), the first causality-aware test coverage 
criterion for deep neural networks, which formalizes neuron interactions and 
quantifies test adequacy based on uncovered causal dependencies. Second, it 
proposes a causal analysis framework for fairness trade-offs in machine 
learning pipelines, enabling systematic identification and quantification of 
causal trade-offs among fairness, robustness, and performance metrics. 
Finally, it develops a causal evaluation approach for large language model 
(LLM)-based code generation, constructing causal graphs over prompt and code 
features to explain and improve LLM outputs through counterfactual analysis.


Date:                   Friday, 4 July 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 5501
                        Lifts 25/26

Committee Members:      Dr. Shuai Wang (Supervisor)
                        Prof. Nevin Zhang (Chairperson)
                        Dr. Dan Xu