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Found 24 papers, 2 papers with code
Terminators: Terms of Service Parsing and Auditing Agents
Terms of Service (ToS) documents are often lengthy and written in complex legal language, making them difficult for users to read and understand.
SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics
With the shift from traditional to digital media, the online landscape now hosts not only reliable news articles but also a significant amount of unreliable content.
Blockchain-based Framework for Scalable and Incentivized Federated Learning
Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets.
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization
We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference Optimization (DPO).
Explainability-Driven Quality Assessment for Rule-Based Systems
This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems based on dataset-driven insights.
On Learning Representations for Tabular Data Distillation
Dataset distillation generates a small set of information-rich instances from a large dataset, resulting in reduced storage requirements, privacy or copyright risks, and computational costs for downstream modeling, though much of the research has focused on the image data modality.
Semantic Interoperability on Blockchain by Generating Smart Contracts Based on Knowledge Graphs
Conclusions: We showed that it is feasible to automatically generate smart contract code based on a semantic KG, in a way that respects the economic rules of blockchain.
A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
We provide the first prototype application of differential privacy with blockchain for vertical federated learning.
Using Large Language Models for Generating Smart Contracts for Health Insurance from Textual Policies
We ascertain LLMs are good at the task (1), and the structured output is useful to validate tasks (2) and (3).
Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic.
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
How can AI be made accountable for its decision-making?
LLM-augmented Preference Learning from Natural Language
Comparing performances with existing methods, we see that pre-trained LLMs are able to outperform the previous SotA models with no fine-tuning involved.
PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace
Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models.
MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment
Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history.
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel.
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
To enable the adoption of the ever improving AI risk prediction models in practice, we have begun to explore techniques to contextualize such models along three dimensions of interest: the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions.
Semantic Modeling for Food Recommendation Explanations
Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations.
Applying Personal Knowledge Graphs to Health
Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems.
Explanation Ontology: A Model of Explanations for User-Centered AI
With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system's AI capabilities.
Explanation Ontology in Action: A Clinical Use-Case
We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl. org/heals/eo).
Directions for Explainable Knowledge-Enabled Systems
Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently.
Foundations of Explainable Knowledge-Enabled Systems
Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
Making Study Populations Visible through Knowledge Graphs
To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations.
Knowledge Integration for Disease Characterization: A Breast Cancer Example
With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current.
