How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos

How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos

Published By: MIT Technology Review, 8/19/2016

Summary: Researchers have developed a machine-learning algorithm that achieves 70% recall in identifying depressed individuals by characteristics of their (pre-diagnosis) Instagram photo posts. This is a great example of a medical development with great potential for benefit (early diagnosis and treatment) that also raises serious concerns (privacy, misuse of the information, misprediction). It’s also an example of Mechanical Turk being used as a research platform.

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Extended Discussion Questions:

  • How could this algorithm be used in practice to produce the most benefit? Who would use it?
  • Can you think of any ways an individual could be harmed by this type of analysis? Who might want to know whether someone is depressed?
  • Why do you think the researchers used Mechanical Turk to recruit participants for their study? What advantages and disadvantages might that have?
  • What did the researchers do to try to avoid bias? Can you think of any other ways the results might be biased by the data selection or experimental methods? What consequences could this have? (This requires having read the article carefully.)

Alternative Article: The original research article gives more details about subject selection, data processing, analysis, etc. that may help you to answer students’ questions.

CSP Global Impact Learning Objectives/EKs:
LO 7.2.1 Explain how computing has impacted innovations in other fields.
LO 7.3.1 Analyze the beneficial and harmful effects of computing.
LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

EK 7.1.2F Crowdsourcing offers new models for collaboration, such as connecting people with jobs and businesses with funding.
EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.
EK 7.3.1A Innovations enabled by computing raise legal and ethical concerns.
EK 7.3.1G Privacy and security concerns arise in the development and use of computational systems and artifacts.
EK 7.3.1J Technology enables the collection, use, and exploitation of information about, by, and for individuals, groups, and institutions.

Other CSP Big Ideas:
3 Data and Information
4 Algorithms

Banner Image: “Network Visualization – Violet – Crop 13”, 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

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