How bad data keeps us from good AI | Mainak Mazumdar

The video discusses the role of AI in the future economy, highlighting its benefits and limitations, particularly in policy decision-making. It emphasizes the impact of biased data on AI decisions and the need for high-quality data. Case studies, including the 2020 US census, illustrate the consequences of data bias. The video stresses the importance of accurate data collection, especially in rural areas, to ensure fair AI models. It concludes with a call to build better data infrastructure for ethical AI.

What are the three key areas that need focus to improve AI's effectiveness in society?

What was the issue with the Duke University model called Pulse?

How does undercounting in the census affect policy decisions?

Why is it important to have a representative database for AI models?

What steps can be taken to ensure that minorities are accurately counted in the census?

What role does data quality play in the effectiveness of AI systems?

What are the implications of excluding rural data in AI decision-making?

How can bias in data lead to wrong decisions in AI applications?

What is the significance of including hard-to-reach populations in data collection?

In what ways can AI benefit from improved data infrastructure?