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Filtering by: Creator Archila, Kevin A. Contributor advisor sim Dietterich, Thomas G. Committee Member Li, Fuxin

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:

Dodge, Jonathan E.

Abstract:

Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...

Resource Type:

Dissertation

Full Text:

ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools
Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Creator:

Zemicheal, Tadesse H.

Abstract:

Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...

Resource Type:

Dissertation

Full Text:

failures. To address these problems, we propose a two-level architecture, SENSOR-DX, for automated quality
The Familiarity Hypothesis: Explaining the Behavior of Deep Learning Methods in Out-of-Distribution Detection

Creator:

Guyer, Alexander

Abstract:

Many object recognition applications require detecting and responding to objects drawn from a different distribution from that of the training data. This task is referred to as out-of-distribution (OOD) detection, and it is often formulated as an outlier detection problem wherein the probability distribution of the known data P(X) is...

Resource Type:

Masters Thesis

Full Text:

responding to objects drawn from a different distribution from that of the training data. This task is