Algorithmic Trade-offs Between Accuracy and Fairness in Responsible AI, with Dr. Shiana Raza
Welcome to another insightful episode of Mutual Connections! I'm your host, Shayan Mashatian, and today we’re diving deep into the world of responsible AI with a distinguished expert, Dr. Shaina Raza. As an applied machine learning scientist at the Vector Institute for Artificial Intelligence, Dr. Raza is a leading voice in AI fairness, bias mitigation, and sustainable AI practices. In this conversation, we explore the ethical considerations of AI, the balance between accuracy and fairness, and the evolving landscape of generative AI. Whether you’re an AI enthusiast, researcher, or simply curious about the future of technology, this episode offers invaluable insights into one of the most critical discussions in the field today.
Algorithmic Trade-offs Between Accuracy and Fairness in Responsible AI, in conversation with:
Dr. Shaina Raza, Applied Machine Learning Scientist, Vector Institute
Highlights of the Conversation:
The Journey to Fairness in AI – The transition from optimizing news recommender systems to realizing the impact of filter bubbles and bias in AI models.
The Accuracy vs. Fairness Trade-off – The challenge of balancing high accuracy with fairness and diversity in AI models.
Bias in AI: Sources and Impact – How bias can arise from data imbalances, algorithmic design, or societal structures, and why it is critical to address it.
Techniques for Bias Mitigation – Various strategies such as adjusting data representation, applying fairness constraints, and fine-tuning algorithms.
The Role of Synthetic Data – Exploring the benefits and risks of using synthetic data to address bias due to a lack of diverse real-world data.
Evaluating Fairness in AI Models – Quantifying fairness using demographic breakdowns and statistical measures to ensure balanced outcomes.
The DeepSeek Effect and Open-Source AI – The rise of open-source AI models, their accessibility, and how they compare to proprietary models in terms of fairness and ethical implementation.
Sustainable AI and Carbon Footprint Considerations – The environmental impact of large AI models and efforts to develop more efficient, lower-emission alternatives.
Adversarial AI and Red Teaming – Using adversarial training techniques to detect misinformation, deepfakes, and unintended biases in AI models.
Future of AI: Where Are We Headed? – The shift from large, general-purpose models to smaller, specialized models tailored for industry-specific applications, and the growing importance of application layers over foundational AI models.
AI development should not be driven by hype but by a commitment to responsible, fair, and ethical practices. As AI continues to evolve, researchers, practitioners, and organizations must prioritize fairness and accountability in its implementation.
Tune in to this thought-provoking episode and join the conversation on the future of AI fairness and responsibility!
