Machine Learning Reveals Lack of Female Screen Time in Top Films

Anna Clark

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Published By: New Scientist, 3/8/2017

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Summary

Shri Narayanan, from the University of Southern California, has developed a new program that runs facial recognition software and voice recognition software simultaneously to analyze gender bias in high-grossing box-office films. This program has analyzed 300 films, and the study shows that women are underrepresented on the big screen. Narayanan is also using machine learning and natural language processing to evaluate film scripts, to explore further biases.

Flesch-Kincaid Grade Level of Article: 12.1

Extended Discussion Questions

How else might facial recognition algorithms, voice recognition algorithms, or natural language processing be used to reveal gender bias in different types of documents?
- How might these technologies be used to reveal other types of bias?
How does this research demonstrate the relationship between data and information?
- Besides the information that arose directly from the study, what other information about the world are the researchers — or you — using to draw conclusions from the results?
If film producers are made more aware of gender biases in screen time or dialogue content, how might this affect the movies you see in the theater?
- How likely are those effects, in your opinion?

Global Impact Learning Objectives:

LO 7.2.1 Explain how computing has impacted innovations in other fields.
LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

Global Impact Essential Knowledge:

EK 7.1.1E Widespread access to information facilitates the identification of problems, development of solutions, and dissemination of results.
EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.

Other CSP Big Ideas:

Idea 3 Data and Information
Idea 4 Algorithms

Banner Image: “Network Visualization – Violet – Offset Crop“, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0.