The Toronto-dominion Bank

Yaqiao Luo

Jesse Cole Cresswell

Atiyeh Ashari Ghomi

Kin Kwan Leung

Barum Rho

Maria Esipova

Maksims Volkovs

Paige Elyse Dickie

Caitlin Messick

Lu Shu

Valentin Tsatskin

Kyle William Hall

Derek Cheung

Kai Wang

A model evaluation system evaluates the extent to which privacy-aware training processes affect the direction of training gradients for groups. A modified differential-privacy ('DP') training process provides per-sample gradient adjustments with parameters that may be adaptively modified for different data batches. Per-sample gradients are modified with respect to a reference bound and a clipping bound. A scaling factor may be determined for each per-sample gradient based on the higher of the reference bound or a magnitude of the per-sample gradient. Per-sample gradients may then be adjusted based on a ratio of the clipping bound to the scaling factor. A relative privacy cost between groups may be determined as excess training risk based on a difference in group gradient direction relative to an unadjusted batch gradient and the adjusted batch gradient according to the privacy-aware training.

Identifying and mitigating disparate group impact in differential-privacy machine-learned models

A model visualization system analyzes model behavior to identify clusters of data instances with similar behavior. For a selected feature, data instances are modified to set the selected feature to different values evaluated by a model to determine corresponding model outputs. The feature values and outputs may be visualized in an instance-feature variation plot. The instance-feature variation plots for the different data instances may be clustered to identify latent differences in behavior of the model with respect to different data instances when varying the selected feature. The number of clusters for the clustering may be automatically determined, and the clusters may be further explored by identifying another feature which may explain the different behavior of the model for the clusters, or by identifying outlier data instances in the clusters.

Visualizing feature variation effects on computer model prediction

The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Generating adaptive textual explanations of output predicted by trained artificial-intelligence processes

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Toronto, Canada

Yaqiao Luo