XAI: Learning Fairness with Interpretable Machine

TL;DR

This event features speakers from KPMG and Serge Masis discussing the adoption of AI in business, ethical concerns, and methods to detect and mitigate bias in machine learning.

Transcript

hey everyone and welcome my name is hayyang i'm a senior data scientist at kpmg initiation tokyo welcome to xai learning fairness with interpretable machine learning this event is co-hosted by deeplearning.ai kpmg initiation tokyo and machine learning tokyo kpmg initiation tokyo is a technological hub of kpmg japan it develops common digital platfo... Read More

Key Insights

• 💗 Adoption of AI in businesses is growing rapidly in 2021, with a shift from limited pilot capacity to functional and fully scalable operational AI systems.
• 👨‍💼 Confidence in the governance and control of AI remains a concern for business executives, particularly in terms of cybersecurity breaches and privacy violations.
• 🌥️ Smaller and medium-sized businesses are more likely to have established codes of ethics for AI compared to large companies, indicating implementation challenges in larger organizations.
• 🖐️ Interpretable machine learning techniques and methods to detect and mitigate bias play a crucial role in building trust in AI models.
• 🎚️ Bias can originate from data quality, systemic biases, and observer biases, and bias mitigation should focus on both model and data levels.
• 🕵️ Various fairness metrics and visualization techniques can be used to detect and quantify bias, enabling fairer decision-making in machine learning models.
• 😒 Pre-processing, in-processing, and post-processing techniques can be applied to mitigate bias in machine learning, depending on the specific use case and requirements.
• 😌 The future of machine learning lies in the seamless integration of interpretability and explainability into models, enabling organizations to pilot AI more reliably and ethically.
• 👨‍💼 Collaboration between business executives, domain experts, and regulators is essential in shaping regulations and standards for ethical AI.

Questions & Answers

Q: Should the responsibility for regulating AI be given to the government or companies?

The responsibility for regulating AI is a shared effort between the government and companies. While businesses often lead the way in shaping regulations, government involvement is necessary to ensure ethical standards are met and to address potential risks and challenges in AI implementation.

Q: Do established industry-wide frameworks exist for defining ethical AI?

Currently, the implementation of ethical AI frameworks is often ad hoc in most industries. However, industries such as financial services and pharmaceuticals are more inclined to work together with regulators and develop governance structures. The need for industry-wide standard frameworks is growing, and businesses and regulators need to collaborate to establish comprehensive ethical guidelines.

Summary & Key Takeaways

• Tim Danley from KPMG Ignition Tokyo discusses the challenges and concerns that business leaders face in adopting AI, emphasizing the importance of ethics and governance in AI implementation.

• Serge Masis, author of "Interpretable Machine Learning with Python," presents an overview of methods to detect and mitigate bias in machine learning, focusing on fairness and interpretability.

• Questions are raised about the responsibility of regulating AI (government vs. companies) and the presence of industry-wide frameworks for AI ethics.