270 citations · 646 across the 26 of their papers we have counts for
7 papers · 1 filter
Contextual Moral Value Alignment Through Context-Based Aggregation
Pierre Dognin, Jesus Rios, Ronny Luss +7
Developing value-aligned AI agents is a complex undertaking and an ongoing challenge in the field of AI. Specifically within the domain of Large Language Models (LLMs), the capabil…
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Q. Vera Liao, Yunfeng Zhang, Ronny Luss +2
Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to ev…
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss +4
In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Gi…
A Formal Framework to Characterize Interpretability of Procedures
Amit Dhurandhar, Vijay Iyengar, Ronny Luss +1
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an abso…
TIP: Typifying the Interpretability of Procedures
Amit Dhurandhar, Vijay Iyengar, Ronny Luss +1
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an abso…