Big Data Problems - Crash Course Statistics

Adriene Hill discusses the potential downsides of big data, focusing on issues like algorithmic bias and privacy concerns. She uses examples such as the husky vs. wolf algorithm and the COMPAS risk assessment tool to illustrate how bias can be introduced. The video also covers privacy laws like GDPR and COPPA, and the challenges of maintaining data privacy in the age of big data. The importance of algorithmic transparency and the responsibility of companies to protect data are emphasized, concluding with a call for caution in the use of big data.

What are some potential downsides of big data as discussed in the text?

How did the algorithm created by the University of Washington and UC Irvine misidentify huskies and wolves?

What are some examples of bias that can be introduced in algorithms due to big data?

What measures can be taken to ensure that data used in algorithms is representative?

What is the significance of the phrase 'garbage in, garbage out' in the context of algorithms?

What is algorithmic transparency and why is it important?

How does the General Data Protection Regulation (GDPR) address privacy concerns related to big data?

What are the implications of the Children's Online Privacy Protection Act (COPPA) for data collection?

Discuss the ethical considerations surrounding the use of DNA databases in criminal investigations.

What steps can individuals take to protect their personal data in the age of big data?