AI and Bias Quiz

Froggy Jumps

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Test your knowledge on AI biases and image cropping debates on Twitter!

Créé par

KEVIN JULIAN DOMINGUEZ AYALA

Colombia

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#	Jeux	Précision	temps
1	Karen 16 Mai 2025	% 57	00:41
2	Diego Vasquez 20 Mai 2025	% 57	01:20
3	alexa 20 Mai 2025	% 57	01:21
4	Luisa Moncada 20 Mai 2025	% 43	00:57
5	VALENTINA CARDENAS MEDINA 19 Mai 2025	% 43	01:33
6	valentina beltran 20 Mai 2025	% 43	01:41
7	Luisa Sánchez 20 Mai 2025	% 14	01:18
8	KEVIN JULIAN DOMINGUEZ AYALA 16 Mai 2025	% 0	00:09

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Froggy Jumps AI and Bias QuizVersion en ligne Test your knowledge on AI biases and image cropping debates on Twitter! par KEVIN JULIAN DOMINGUEZ AYALA 1 What was the main bias discovered in Twitter’s automatic image-cropping algorithm? a Favoring images with light backgrounds over dark ones b Prioritizing faces with light skin, youthful appearance, and feminine features c Always selecting the geometric center of the photo 2 Who led the analysis that uncovered the bias in Twitter’s automatic image‑cropping algorithm? a Joy Buolamwini b Timnit Gebru c Bogdan Kulynych 3 What method did the researcher use to demonstrate the algorithm’s bias? a Surveying Twitter users about their image‑cropping experiences b Generating thousands of synthetic faces with varied attributes c Manually annotating real user photos for bias 4 How did Twitter respond after confirming the bias in its image‑cropping algorithm? a Denied any bias and kept the algorithm unchanged b Disabled automatic image cropping in the mobile app c Released a new algorithm without further testing 5 According to the “Gender Shades” study, what was the error rate when identifying dark‑skinned women? a 7.0% b 34.7% c 12.0% 6 What was the official name of Twitter’s challenge to detect bias in its algorithm? a Algorithmic Bias Bounty Challenge b Transparency in Algorithms Contest c AI Ethics Hackathon 7 What key ethical lesson does the Twitter cropping case illustrate for AI development? a Algorithms should prioritize efficiency over fairness b Continuous auditing and oversight are essential to prevent bias c Once deployed, algorithms should remain static to ensure consistency

Froggy Jumps

AI and Bias QuizVersion en ligne

Test your knowledge on AI biases and image cropping debates on Twitter!

par KEVIN JULIAN DOMINGUEZ AYALA

What was the main bias discovered in Twitter’s automatic image-cropping algorithm?

Favoring images with light backgrounds over dark ones

Prioritizing faces with light skin, youthful appearance, and feminine features

Always selecting the geometric center of the photo

Who led the analysis that uncovered the bias in Twitter’s automatic image‑cropping algorithm?

Joy Buolamwini

Timnit Gebru

Bogdan Kulynych

What method did the researcher use to demonstrate the algorithm’s bias?

Surveying Twitter users about their image‑cropping experiences

Generating thousands of synthetic faces with varied attributes

Manually annotating real user photos for bias

How did Twitter respond after confirming the bias in its image‑cropping algorithm?

Denied any bias and kept the algorithm unchanged

Disabled automatic image cropping in the mobile app

Released a new algorithm without further testing

According to the “Gender Shades” study, what was the error rate when identifying dark‑skinned women?

7.0%

34.7%

12.0%

What was the official name of Twitter’s challenge to detect bias in its algorithm?

Algorithmic Bias Bounty Challenge

Transparency in Algorithms Contest

AI Ethics Hackathon

What key ethical lesson does the Twitter cropping case illustrate for AI development?

Algorithms should prioritize efficiency over fairness

Continuous auditing and oversight are essential to prevent bias

Once deployed, algorithms should remain static to ensure consistency

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AI and Bias Quiz

Froggy Jumps

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

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AI and Bias QuizVersion en ligne

par KEVIN JULIAN DOMINGUEZ AYALA

What was the main bias discovered in Twitter’s automatic image-cropping algorithm?

Who led the analysis that uncovered the bias in Twitter’s automatic image‑cropping algorithm?

What method did the researcher use to demonstrate the algorithm’s bias?

How did Twitter respond after confirming the bias in its image‑cropping algorithm?

According to the “Gender Shades” study, what was the error rate when identifying dark‑skinned women?

What was the official name of Twitter’s challenge to detect bias in its algorithm?

What key ethical lesson does the Twitter cropping case illustrate for AI development?

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