Using ML responsibly and ethically
Graded Quiz • 30 min Quiz10 Questions Week 3
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Using ML responsibly and ethically

Graded Quiz • 30 min Quiz10 Questions Week 3
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Q:

Google has developed a set of AI principles that govern its research and product development and affect its business decisions. One of the AI principles is to avoid creating or reinforcing unfair bias. Why does a company need to adhere to a set of agreed-upon standards when working with AI?

Q:

Your company has decided to experiment with artificial intelligence to improve business processes and decision-making efficiency. Your company has decided to follow the AI principles developed by Google, and one of the AI principles is to avoid creating or reinforcing unfair bias. Why is it important to avoid creating unfair biases in machine learning models?

Q:

You are a scientist at a hospital studying the likelihood of common health conditions in your community. You want to use machine learning to predict which patients might have an increased probability of heart disease. You already have decided to use recently updated patient data including: age range, gender, medical history, weight, and how often, if ever, a person smokes. In this scenario, where and why might you expect to find reporting bias?

Q:

An international airport has tasked a technology company to create a machine learning model that can identify potential criminals entering the country. The airport has provided the company with images of criminal and non-criminal residents, with their consent. The airport has long queues at immigration and hopes that introducing this electronic system will improve efficiency and the traveler experience. In this scenario, what assumption is being made that is an example of automation bias?

Q:

Google has developed a set of AI principles that govern its research and product development and impact its business decisions. One of the AI principles is to avoid creating or reinforcing unfair biases. However, what should AI applications not do according to Google’s AI principles?

Q:

An owner of a national live sports TV channel has had ongoing success for 15+ years. Recently, the viewership on the channel has decreased due to viewers moving to online streaming platforms. The owner finds data indicating social media discussion for the sporting events on the channel is increasing every year, and argues that viewers are very much still engaged. The owner uses this data to justify the channel to advertisers who can buy advertisement time across the day. In this scenario, what is an indisputable example of confirmation bias?

Q:

You are a data collector at a movie review aggregation company that is aimed toward family and children’s movies. As audience members are leaving the screening room at the cinema, you survey their opinion of the movie on an alphabetical grading scale. Your company only surveys audiences within the first week of a movie’s release and then locks the grades. The aggregate grade is provided to film production companies who want to improve future family and children’s films. In this scenario, what would be an example of selection bias?

Q:

You are a translation vendor manager who has been tasked by a publishing company to translate historical Finnish texts. The translation is from Finnish, which has no gender-distinctive pronouns, to English. To save time, your experienced Finnish team decides to use a popular machine learning translation tool for longer texts while they translate the shorter texts manually. In this scenario, where might you expect an increased amplification of biases?

Q:

A survey is delivered over the internet through links within ads that appear on the homepage of popular newspapers’ websites. The survey asks sensitive questions about readers’ health and medical history. What type of bias is caused by not stating that surveyees might only include people who read the front page and not those who might consume primarily sports-related news?

Q:

A survey is delivered over the internet through links within ads that appear on the homepage of popular newspapers’ websites. The survey asks sensitive questions about readers’ health and medical history. It can be argued that selection bias exists in this scenario. What justification is there for this reasoning?

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