"Datafication" is a term used to describe how all aspects of our lives are being turned into datapoints. Whether through the collection of our likes, shares, and ratings on social media and streaming apps, or through the harvesting of physical data from devices like smartphones and smartwatches, datafication is what powers artificial intelligence.

Case Study: The Datafication of Education

A study by Atlas VPN found that 98% of iOS educational apps gather user data, with each app on average collecting information from over 8 data segments. This can include names, emails, phone numbers, location, payment information, and search history. Duolingo topped the list by collecting user data across 19 segments. Google Classroom collects data from 17 segments.

The primary purposes for collecting data were app functionality (86%), analytics (80%), personalisation (56%), and developer's advertising or marketing (54%). However, 24% of the apps also used collected data for third-party advertising.

Where Does All the Data Go?

Companies gather large amounts of information for various reasons, and AI plays a significant role:

  1. Targeted Advertising: AI algorithms analyse collected data to predict the most effective ads for each user, leading to intrusive and privacy-invading ad experiences.
  2. Competitive Research: AI identifies gaps in the market or predicts future trends, but this can lead to aggressive competition or unethical business practices.
  3. Surveillance and Profiling: AI processes vast amounts of data to create comprehensive user profiles, raising significant privacy concerns.
  4. Third-Party Data Sharing: Companies share user data with data brokers, advertisers, or business partners without explicit consent.
  5. Unfair Competitive Advantages: Companies with vast user data leverage AI to predict and influence behaviour, potentially creating monopolies.

Teaching AI Ethics

  • History: How does datafication impact the way we study and interpret historical events?
  • English: How does datafication influence the way literature is analysed and interpreted?
  • Mathematics: How has datafication transformed the study of mathematics and careers in maths?
  • Environmental Science: How does datafication impact the study of environmental systems?
  • Visual Arts: What are the ethical implications of using data-driven methods in artistic practice?
  • Geography: What are the ethical considerations in using data-driven methods to analyse geographical information?