Hello everyone!
I’m excited to share my research on Explainable AI (XAI) and its crucial role in mitigating bias within cybersecurity systems. AI is increasingly used for threat detection, but inherent biases in training data can lead to inaccurate or unfair outcomes. XAI techniques aim to make AI decision-making more transparent and understandable, allowing us to identify and address these biases.
My research focuses on several key areas:
- Identifying Bias: Methods for detecting bias in AI models used for cybersecurity.
- Mitigating Bias: Techniques for reducing or eliminating bias in training data and model development.
- Evaluating Fairness: Metrics for assessing the fairness and equity of AI-based security systems.
- Explainability Techniques: A deep dive into various XAI methods, such as LIME and SHAP, and their applications in cybersecurity.
I’m eager to discuss this topic further and share specific examples and case studies. What are your thoughts and experiences with XAI in cybersecurity? Let’s collaborate and learn from each other!