Popcorn Hack 1:

Provide an example of a movie, TV show, video game, or software that demonstrates bias and specify who is affected by it. Explain a potential cause of this bias.

  • Super Mario demonstrates bias against women. This could be caused by Princess Peach being frequently depicted as a damsel in distress, reinforcing gender stereotypes where women need to be rescued by male heroes.

Popcorn Hack 2:

Think about a time when you felt a technology didn’t work well for you. What was the issue, and how did it make you feel? Write a short paragraph describing the experience and suggest one way the technology could be improved to be more inclusive.

  • When I go to charge my phone, my phone’s outlet never works and I can’t charge it. It made me angry because I was tired and had to charge my phone so my alarm goes off in the morning. I lost sleep and felt drowsy in the morning because of it. Technology like this could be more inclusive if it had better chargers. For example the touch charging that some new phones have.

Popcorn Hack 3:

Imagine you’re designing a fitness tracking app. How could bias sneak into your app’s recommendations or performance evaluations? Think about users with different physical abilities, ages, or health conditions. What features could you add to ensure the app is fair and inclusive for all users?

  • Bias: If the app sets default fitness goals based on general population data, it may not consider users with disabilities or mobility limitations, leading to unrealistic targets.

  • Bias: Users with diseases or conditions might be evaluated unfairly if the app only considers steps taken or calories burned as measures of success.

  • Solution/Feature: Allow users to set goals based on their individual abilities rather than default metrics.
  • Solution/Feature: Provide exercise recommendations that adjust based on mobility, energy levels, and medical conditions.

Homework Hack 1:

  • Choose a digital tool, app, or website you use regularly. It could be a social media platform, a shopping site, or a streaming service.

  • Identify Potential Bias: Are there any patterns in the recommendations or interactions that might suggest bias? Does the platform cater well to different user groups (e.g., age, gender, language, accessibility)?

  • Analyze the Cause: What might be causing this bias? Consider data collection, algorithm design, or lack of diverse testing.

  • Propose a Solution: Suggest one way the developers could reduce bias and make the platform more inclusive.

Digital Tool Chosen: YouTube

  • Identifying Potential Bias: YouTube’s recommendation system tends to reinforce certain viewpoints by showing users more of the same type of content they have previously watched. This can create “filter bubbles” where users are mainly exposed to one perspective. Additionally, content from larger creators often gets prioritized over smaller, diverse voices.

  • Analyzing the Cause: This bias likely stems from YouTube’s algorithm, which prioritizes engagement metrics like watch time and click-through rates. If users interact mostly with specific types of content, the algorithm continues to push similar videos, limiting exposure to diverse perspectives.

  • Proposing a Solution: YouTube could introduce a feature that occasionally recommends content outside a user’s typical preferences. They could also adjust the algorithm to promote smaller creators and diverse viewpoints, ensuring a broader range of content is surfaced.