ServiceNow Research

Explainability

Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. …
Conference on Human Factors in Computing Systems (ACM-CHI),  2024.
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Explaining Graph Neural Networks Using Interpretable Local Surrogates
Explaining Graph Neural Networks Using Interpretable Local Surrogates
We propose an interpretable local surrogate (ILS) method for understanding the predictions of black-box graph models. Explainability …
Workshop at the International Conference on Machine Learning (ICML),  2023.
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Explainable, Sensible and Virtuous Workplace Chatbots
We outline three research directions towards the practical implementation of explainable, sensible and virtuous chatbots for the …
Montreal AI Symposium (MAIS),  2022.
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Explaining by Example: A Practitioner’s Perspective
Black-box machine learning (ML) models have become increasingly popular in practice. They can offer great performance, especially in …
Montreal AI Symposium (MAIS),  2022.
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Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable attention within the research community given the importance of …
International Conference on Computer Vision (ICCV),  2021.
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RelatIF: Identifying Explanatory Training Examples via Relative Influence
RelatIF: Identifying Explanatory Training Examples via Relative Influence
In this work, we focus on the use of influence functions to identify relevant training examples that one might hope …
International Conference on Artificial Intelligence and Statistics (AISTATS),  2020.
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