Search Results for author:
Found 8 papers, 3 papers with code
Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks
While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems.
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies
Our findings highlight internal consistency issues wherein participants perceived language explanations to be significantly more usable, however participants were better able to objectively understand the decision making process of the car through a decision tree explanation.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server.
A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting
Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i. e., goals and constraints) from language (p < 0. 05).
Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers
The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users.
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy.
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior.
Controllable Neural Story Plot Generation via Reward Shaping
Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story.
