Accountable & Explainable Methods for Complex by Pepa Atanasova (.PDF)

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Accountable and Explainable Methods for Complex Reasoning over Text by Pepa Atanasova
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Overview: This thesis presents research that expands the collective knowledge in the areas of accountability and transparency of Machine Learning (ML) models developed for complex reasoning tasks over text. In particular, the presented results facilitate the analysis of the reasons behind the outputs of ML models and assist in detecting and correcting for potential harms. It presents two new methods for accountable ML models; advances the state of the art with methods generating textual explanations that are further improved to be fluent, easy to read, and to contain logically connected multi-chain arguments; and makes substantial contributions in the area of diagnostics for explainability approaches. All results are empirically tested on complex reasoning tasks over text, including fact checking, question answering, and natural language inference. A major concern with Machine Learning (ML) models is their opacity. They are deployed in an increasing number of applications where they often operate as black boxes that do not provide explanations for their predictions. Among others, the potential harms associated with a lack of understanding of the models’ rationales include privacy violations, adversarial manipulations, and unfair discrimination. In Computer Science, the decision-making process of ML models has been studied by developing accountability and transparency methods. Accountability methods, such as adversarial attacks and diagnostic datasets, expose vulnerabilities in ML models that could lead to malicious manipulations or systematic faults in their predictions.
Genre: Non-Fiction > Tech & Devices

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