A Road-map Towards Explainable Question Answering A Solution for Information Pollution
Project Overview
The document explores the integration of generative AI in education, highlighting the development and significance of Explainable Question Answering (XQA) systems to combat information pollution on the Web. It underscores the necessity for transparency, accountability, and fairness in AI applications, especially in critical areas affecting human lives, such as biomedical and life sciences. The paper identifies the challenges XQA systems face and outlines user expectations for these systems, which should not only deliver answers but also provide clear explanations to allow users to evaluate the credibility and reliability of the information received. By emphasizing the role of generative AI in enhancing educational tools and resources, the document suggests that XQA systems can significantly improve information dissemination and understanding, ultimately leading to better-informed users and more effective learning outcomes.
Key Applications
Explainable Question Answering (XQA) systems
Context: Educational contexts where users need to make informed decisions based on AI-generated answers, particularly in high-stakes areas like healthcare and biomedical fields.
Implementation: XQA systems utilize explainable computational models and interfaces to provide users with transparency in reasoning and answer selection.
Outcomes: Enhanced trust in AI systems through explanation of answers, improved decision-making capabilities for users, and the ability to discern credible information from misinformation.
Challenges: Complexity in providing clear, understandable explanations, ensuring the system is fair and unbiased, and overcoming the existing black-box nature of AI systems.
Implementation Barriers
Technical Barrier
Existing AI systems are often black boxes that do not explain how they arrive at answers, leading to issues with trust and understanding.
Proposed Solutions: Developing XQA systems that provide clear explanations of the reasoning process and the reliability of sources.
Ethical Barrier
Bias in AI systems can lead to discriminatory information and loss of trust among users.
Proposed Solutions: Implementing fairness and accountability measures in AI design, ensuring diverse training data and regular audits.
User Experience Barrier
Users may be overwhelmed with information and explanations, making it difficult to discern key insights.
Proposed Solutions: Creating user-friendly interfaces that summarize key information and facilitate easier navigation through explanations.
Project Team
Saeedeh Shekarpour
Researcher
Faisal Alshargi
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Saeedeh Shekarpour, Faisal Alshargi
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai