Interpretable AI, with Dr. Faiza Khan Khattak
How can we build AI systems that are fair, explainable, and truly responsible?
In this episode of the #WiAIR podcast, we sit down with Dr. Faiza Khan Khattak, the CTO of an innovative AI startup, with a rich background in both academia and industry. From fairness in machine learning to the realities of ML deployment in healthcare, this conversation is packed with insights, real-world challenges, and powerful reflections.
ToC:
[00:00] Intro
[01:37] Faiza’s Journey in AI
[07:50] Exploring Fairness in AI
[11:17] The Role of Mentorship
[13:27] Transitioning Between Academia, Industry, and Entrepreneurship
[17:02] Challenges Faced by Women in AI
[22:01] MLHOps in Healthcare
[24:30] Operationalizing ML Models in Healthcare
[27:25] Exploring Explainability in AI Models
[31:31] Risks of AI in Healthcare
[33:23] Addressing Social Bias in AI
[38:40] The Role of Reasoning in Interpretability
[41:04] Generalizability of Reasoning Across Models
[46:25] Machine Unlearning and Bias Mitigation
[52:57] Adversarial Attacks and Bias Induction
REFERENCES:
[21:40] MLHOps: Machine Learning Health Operations (https://ieeexplore.ieee.org/abstract/...)
[33:33] Using Chain-of-Thought Prompting for Interpretable Recognition of Social Bias (https://openreview.net/forum?id=QyRga...)
[35:09] Dialectic Preference Bias in Large Language Models (https://ojs.aaai.org/index.php/AAAI-S...)
[41:42] The Impact of Unstated Norms in Bias Analysis of Language Models (https://arxiv.org/abs/2404.03471)
[46:26] Can Machine Unlearning Reduce Social Bias in Language Models? (https://aclanthology.org/2024.emnlp-i...)
[52:58] BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models (https://arxiv.org/abs/2405.04756v1)
👉 Whether you're an AI researcher, a developer working on LLMs, or someone passionate about Responsible AI, this episode is for you.
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