Types of Computing Bias - Tarun

• Data Bias: The data does not accurently represent the values of the real world ex. If data is taken from a sample size that doesn’t reflect the actual population - If you wanted data to represent the population in America but your sample that is being surveyed is from a Texas. The population in Texas does not accurately reflect the entire population of America.
• Human Bias: Those who make programs may be influenced by their own biases ex. If a development team are experts in using a certain language and their algorithm demonstrates that language, they will feel that people who specialize in that language are qualified and better. This is essentially bringing in their personal biases and applying to a larger amount of people.

Explicit data vs Implicit data: - Pranavi

Explicit data:

• takes the data that you give
• When watching a video, and it asks “are you enjoying this?”, and you respond with either a thumbs up or down, you are giving them explicit data

Implicit data:

• When you watch or search up certain things, data can be deduced on what is the “norm” for the person

Example: Netflix

• When browsing through Netflix, they show Netflix exclusives, they do this because they want your subscriptions
• showing the netflix exclusives is the bias in this scenario

Popcorn Hack:

In what other applications could have intential bias?

Intentional Bias vs Unintentional Bias - Tanvi

Example 1: Hypothetical Loan company

• Suppose a software was created to assist loan officers, and certain trends of successful loans were taken
• If people are rejected of those who don’t fit in their trends of either age, gender, race, etc.
• This software is biased in the way that it only chooses candidates who will have higher chances in successful loans

Example 2: Candy Crush vs Call of Duty

• Call of Duty is geared towards the teenage boy demographic, 18-24, with more grunge type of music
• Candy Crush is more visually appearing to younger audience as it includes pictures of candies and playful music
• This is biased as the games include aspects and characteristics that will seem appealing to a specific audience

Popcorn Hack:

How is their unintentional bias in apps such as TikTok or Instagram or otehr social media apps?

Mitigation Strategies - Shubhay

• Utilize data from various sources
• Pre-Processing: A way to check the inputs for bias before it is being used as data
• In-processing: This algorithm changes the data during analysis of the data to keep the data consistent
• Post-Processing: # step check to make sure the model is fair and accurate
  • Input Correction: This strategy makes adjustments to the data to make the data more comparable
  • Classifier Correction: polishing and adjusting the algorithm after it has been trained to reduce the biases
  • Output Correction: The predictions made by the model is modified to eliminate biases

Homework:

1. Is bias enhancing or intentionally excluding?

Bias is neither enhancing or intentionally excluding. It really depends on the situation bias is being put in place. Some examples of enhancing bias is in targeted advertising as many modern browsers go off of user data to put advertisements towards the user’s interests. An example of excluding bias is the marginalization/ignorance of other viewpoints. If a group were to give out a survey and only take in data that represented their group positively it would be intentionally excluding.

1. Is bias intentionally harmful/hateful?

Depending on what the bias it is not harmful or hateful generally speaking. It once again depends on the situation, harmful bias is linked to discrminiatory ideologies; however, something such as a preference for a flavor of candy is not harmful nor hateful.

1. During software development are you receiving feedback from a wide variety of people? It depends on the environment, but I will assume this is referring to industry level work.

Typically yes you would be (assuming industry) for people receive feedback from their users, their shareholders / clients, peer reviews as seen in our class, and asking senior developers / their boss on what they believe is the best course of action.

1. What are the different biases you can find in an application such as Youtube Kids? Some biases you might see in Youtube Kids could be

• Algorithmic bias, if an algorithm recognizes the viewer enjoys a specific type of content it may prioritize that specific type of content and may lead to a limited and closed variety of content.
• Biased Content, it's completely possible that a video could have biased unsourced information that is not inhereitly true, on the less severe case this could be just wrong information, on the more servere it may contain themes of offensive content or ideologies

Answer in complete sentences, due Sunday 11:59 pm