Explaining black box algorithms: epistemological challenges and machine learning solutions
Bibliographic record
Abstract
<p>This dissertation seeks to clarify and resolve a number of fundamental issues surrounding algorithmic explainability. What constitutes a satisfactory explanation of a supervised learning model or prediction? What are the basic units of explanation and how do they vary across agents and contexts? Can reliable methods be designed to generate model-agnostic algorithmic explanations? I tackle these questions over the course of eight chapters, examining existing work in interpretable machine learning (iML), developing a novel theoretical framework for comparing and developing iML solutions, and ultimately implementing a number of new algorithms that deliver global and local explanations with statistical guarantees. At each turn, I emphasise three crucial desiderata: algorithmic explanations must be causal, pragmatic, and severely tested.</p>\n\n<p>In Chapter 1, I introduce the topic through real world examples that vividly demonstrate the ethical and epistemological imperative to better understand the behaviour of black box models. A literature review follows in Chapters 2 and 3, where I situate the project at the intersection of critical data studies, philosophy of information, and computational statistics. In Chapter 4, I examine conceptual challenges for iML that result in misleading, counterintuitive explanations. In Chapter 5, I propose a formal framework for iML – the explanation game – in which players collaborate to find the best solution(s) to explanatory questions through a gradual procedure of iterative refinements. In Chapter 6, I introduce a novel test of conditional independence that doubles as a flexible measure of global variable importance. In Chapter 7, I combine feature attributions and counterfactuals into a single method that retains and extends the axiomatic guarantees of Shapley values while rationalising results for agents with well-defined preferences and beliefs. I conclude in Chapter 8 with a review of my results and a discussion of their significance for data scientists, policymakers, and end users.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".