32 citations · 64 across the 12 of their papers we have counts for
14 papers
"I didn't know how to read a map, but now I can": TouchingSpace, an Audio-Haptic Map for Blind and Low-Vision Readers
Li Liu, Yihe Wang, Jiaming Qu +3
Accessible map systems either make a layout explorable by hand or convey information through speech; few combine both to support pre-travel spatial understanding for blind and low-…
From Anonymous Shapes to Named Places: A Tool for Braille and Place-Semantic Annotation of Tactile Maps
Li Liu, Ashmita Dua, Jiaming Qu +2
On a 3D-printed tactile map, a building felt under the finger is an anonymous shape: touch alone cannot tell which footprint is which, and a spoken description cannot reliably poin…
Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning
Olivia Peiyu Wang, Sanna Wong-Toropainen, Daneshvar Amrollahi +4
Large Language Models (LLMs) achieve strong performance on reasoning tasks, but whether this reflects faithful logical inference or heuristic approximation remains unclear. We stud…
Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
Olivia Peiyu Wang, Leilani H. Gilpin
The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit from AI that can reason over…
Learning to Draw ASCII Improves Spatial Reasoning in Language Models
Shiyuan Huang, Li Liu, Jincheng He +1
When faced with complex spatial problems, humans naturally sketch layouts to organize their thinking, and the act of drawing further sharpens their understanding. In this work, we…
Touching Space: Accessible Map Exploration Through Conversational Audio-Haptic Interaction
Li Liu, Jiaming Qu, Marc Jowell Bagaoisan +2
Most existing assistive navigation tools focus on providing real-time guidance for Blind and Low-Vision (BLV) people, but few support building a holistic spatial understanding of u…
ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering
Priyesh Vakharia, Abigail Kufeldt, Max Meyers +2
Neurosymbolic approaches can add robustness to opaque neural systems by incorporating explainable symbolic representations. However, previous approaches have not used formal logic…
Anticipatory Thinking Challenges in Open Worlds: Risk Management
Adam Amos-Binks, Dustin Dannenhauer, Leilani H. Gilpin
Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As…
The XAISuite framework and the implications of explanatory system dissonance
Shreyan Mitra, Leilani Gilpin
Explanatory systems make machine learning models more transparent. However, they are often inconsistent. In order to quantify and isolate possible scenarios leading to this discrep…
Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning
Cosmin Badea, Leilani Gilpin
We propose an ontology of building decision-making systems, with the aim of establishing Meta-Decision-Making for Artificial Intelligence (AI), improving autonomy, and creating a f…
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI
Leilani H. Gilpin, Andrew R. Paley, Mohammed A. Alam +2
There is broad agreement that Artificial Intelligence (AI) systems, particularly those using Machine Learning (ML), should be able to "explain" their behavior. Unfortunately, there…
A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation
Jason R. Wilson, Leilani Gilpin, Irina Rabkina
There is a need for socially assistive robots (SARs) to provide transparency in their behavior by explaining their reasoning. Additionally, the reasoning and explanation should rep…
Explaining Explanations to Society
Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter +1
There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government…
Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan +3
There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often ca…