5.3 Computing Bias • 15 min read

Big Idea 5.3 Computing Bias- Tanay

Overview/Definition: Computing Biasses are the numerous Biasses in application that are based on human prefrences.

• Computing innovations can reflect existing human biases because of biases written into the algorithms or biases in the data used by the innovation
• Programmers should take action to reduce bias in algorithms used for computing innovations as a way of combating existing human biases
• Biases can be embedded at all levels of software development

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?

YouTube, Spotify, Facebook, among many other streaming/media platforms that use intentional bias to grab people’s attention.

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?

The TikTok or Instagram algorithms that recommend to users certain posts in their feeds may be slightly inaccurate, or promote certain things over another. For example, if a user usually looks at posts of food/cooking, but then accidentally likes a post that has a picture of men without shirts, then the biased algorithm may continue to recommend pictures of men without shirts instead of the food posts.

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 mostly enhancing. This is because bias can be used to enhance user experience, perhaps targeting some game towards a particular subgroup of the population. Using bias, the video game may be more successful in advertising since that particular subgroup will be more attracted to the game. However, bias can on some occasions exclude some. This often results from surveys or elections only reaching certain people, such as in the case of election fraud or other cases data is not entirely accurate. This is mostly not intentional, however.

1. Is bias intentionally harmful/hateful?

No, bias does not generally put down one group, but it may result in certain things being promoted more than others and certain groups of people benefiting from such innovations more.

1. During software development are your receiving feedback from a wide variety of people?

During software development, you are typically not receiving feedback from a wide variety of people. This is because the people that you go to for feedback are generally other software developers and programmers, who are typically buff Indian dudes or nerdy Chinese guys with glasses, checkered shirts, and a balding head.

1. What are the different biases you can find in an application such as Youtube Kids?

In Youtube Kids, videos that can be found are normally targeted towards kids. The developers may therefore only promote videos that are more likely to be “child-oriented” on the site, including things such as nursery rhymes or children’s cartoons. Biases in such applications will mostly include those that limit the content you can see.

Answer in complete sentences, due Sunday 11:59 pm