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AI Governance for Trustable AI, with Koo Ping Shung, Part 1
This conversation between Jon Scheele and data scientist Koo Ping Shung is broken into two parts. In part one, we discuss the complexities of AI governance and the need for expertise across several disciplines to make it effective. We discuss bias in AI and the importance of transparency and a review mechanism to address that bias. And we discuss how it's up to all of us to gain AI literacy, so that we can ensure that AI works for us rather than against us. So enjoy part one.
The conversation explores the topic of AI governance and how to build trustable AI. Koo Ping Shung discusses the evolution of the data science industry and the increasing focus on AI governance. He emphasizes that bias in AI cannot be completely eliminated but can be reduced through awareness and transparency. Koo suggests that AI governance committees should include individuals with technical, legal, policy-making, economics, and audit backgrounds. He also highlights the importance of AI literacy for consumers and the need for dedicated channels to address concerns and appeals.
Keywords
AI governance, trustable AI, bias, data science, evolution, transparency, awareness, AI literacy, consumer concerns, appeals, data, reporting, performance metrics, visualization, AI, governance
Takeaways
- AI governance is essential to ensure the responsible and ethical use of AI.
- Bias in AI cannot be completely eliminated but can be reduced through awareness and transparency.
- AI governance committees should include individuals with diverse backgrounds, including technical, legal, policy-making, economics, and audit.
- AI literacy is crucial for consumers to understand and protect themselves from potential abuses.
- Dedicated channels should be established for consumers to raise concerns and appeals regarding AI decisions.
01:22 The Evolution of the Data Science Industry and the Rise of AI
08:06 The Role of Committees in AI Governance
09:37 Addressing Bias in AI
23:16 Creating Effective AI Governance Committees
26:47 Establishing Dedicated Channels for Consumer Concerns in AI
View all episodes
By Jon ScheeleThis conversation between Jon Scheele and data scientist Koo Ping Shung is broken into two parts. In part one, we discuss the complexities of AI governance and the need for expertise across several disciplines to make it effective. We discuss bias in AI and the importance of transparency and a review mechanism to address that bias. And we discuss how it's up to all of us to gain AI literacy, so that we can ensure that AI works for us rather than against us. So enjoy part one.
The conversation explores the topic of AI governance and how to build trustable AI. Koo Ping Shung discusses the evolution of the data science industry and the increasing focus on AI governance. He emphasizes that bias in AI cannot be completely eliminated but can be reduced through awareness and transparency. Koo suggests that AI governance committees should include individuals with technical, legal, policy-making, economics, and audit backgrounds. He also highlights the importance of AI literacy for consumers and the need for dedicated channels to address concerns and appeals.
Keywords
AI governance, trustable AI, bias, data science, evolution, transparency, awareness, AI literacy, consumer concerns, appeals, data, reporting, performance metrics, visualization, AI, governance
Takeaways
- AI governance is essential to ensure the responsible and ethical use of AI.
- Bias in AI cannot be completely eliminated but can be reduced through awareness and transparency.
- AI governance committees should include individuals with diverse backgrounds, including technical, legal, policy-making, economics, and audit.
- AI literacy is crucial for consumers to understand and protect themselves from potential abuses.
- Dedicated channels should be established for consumers to raise concerns and appeals regarding AI decisions.
01:22 The Evolution of the Data Science Industry and the Rise of AI
08:06 The Role of Committees in AI Governance
09:37 Addressing Bias in AI
23:16 Creating Effective AI Governance Committees
26:47 Establishing Dedicated Channels for Consumer Concerns in AI