Big Idea 5.3 Computing Bias • 14 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?

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?
2. Is bias intentionally harmful/hateful?
3. During software development are your receiving feedback from a wide variety of people?
4. What are the different biases you can find in an application such as Youtube Kids?

Answer in complete sentences, due Sunday 11:59 pm

Bias in apps like YouTube Kids can be about showing some stuff more or leaving out certain things. It might happen by accident or on purpose to be unfair. This can show that bias can enhance certain views, opinions, and even intake, but can exclude things for the better. Having kids view content that is inappropriate for their age may be better. Bias can be good or bad especially based on what a person experienced in their life.

Sometimes, bias can be mean on purpose, promoting hate or harm towards certain groups. But bias can also be unintentional as more views and opinions make their way online. So, we should focus on decreasing bias especially if it is unintentional.

When making apps, it’s important for developers to get feedback from different people to make sure it’s fair and works well for everyone. It can also help to fix bias and get rid of certain content that users may not find appropriate. User feedback is important especially since they are the ones viewing and potentially using your code. We get feedback that helps. We recieve feedback from friends, peers, and teachers. Live reviews are really helpful since much feedbakc is given.

YouTube Kids might show some things more because of biases, like only from certain cultures or only for specific genders as well. It’s important to fix this so everyone gets a fair experience. However, based on the content for which the kid likes, such biases may not be so negative. You tube can also have bias based on age. Todlers versus elementry kids. They most probably recieve different content yet use the same platform, which is due to bias.