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Lab #Experiential Learning
1. Play the following game on Kahoot:
https://create.kahoot.it/share/thought-experiment-trolley-problem-ai-in-autonomous-
vehicles/3bf7c44a-f675-491b-a5d0-b0ad38f04d00
2. Play the Facework game! Understand what it means to see like a machine and how machines
might fail. If you have a smiling face that matches the machine’s data then you could be
hired as a babysitter otherwise get ready to become a prison guard! Go to https://facework.app/
3. Have a job interview with an AI HR manager. “An Interview with ALEX” is a 12-minute guided, interactive
experience in the browser that immerses the spectator in a job interview with ALEX, a sophisticated
artificial intelligence HR hired by a hypothetical tech giant known as “Open Mind Playground.” The
experience reveals how companies can use machine learning to conceal the true purpose of a job,
extract the maximum amount of labour from its employees, and censor information under the radar—
by gamifying the work process, applying filters to employees' information intake, and generating
customised distractions. Go to
https://carriesijiawang.com/interview/#two
4. Understand the future of automated media manipulation by either uploading your own
photos or trying to detect fake videos. Deep Angel - image manipulation technology
(http://deepangel.media.mit.edu/)
5. Check out the gender bias in Google Translate. Translate the following statements to
Hindi and check the bias
a. The nurse is eating.
b. The doctor is walking.
c. The mathematician is working.
You can read more about this at https://scroll.in/article/991275/google-translate-is-
sexist-and-it-needs-a-little-gender-sensitivity-training
Class Activity #Collaboration & Teamwork
Divide the class into groups of 3 students each. [CBSE Handbook]
Ask students to bring any printed media like news articles, advertisements, or social media posts, and identify
instances of bias based on factors like race, gender, or socio-economic status.
Note down three points on how bias can influence perceptions and stereotypes.
Answers
Exercise (Section A)
A. 1. c 2. c 3. b 4. b 5. b 6. a 7. b 8. b 9. b 10. a
B. 1. Prejudice 2. Training 3. Dataset 4. Ethical 5. Robustness
6. Ethical 7. Ethics 8. Design 9. Cognitive bias 10. fair
C. 1. True 2. False 3. False 4. False 5. True 6. False
7. True 8. False 9. False 10. True
AI Ethics and Values 425

