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?

Google search result biases that highlight websites that pay Google in ad money or have paid for a higher spot in search results. 

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?

There is bias in social media through the cyclic nature of recommendations. Every time you watch a certain reel or like a certain post, more of the same content will be fed to you through your feed. 

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

1. Is bias enhancing or intentionally excluding?

Bias can manifest in different ways. It can be unintentional and emerge as a result of inherent human biases in data or algorithms, enhancing certain patterns or groups. On the other hand, bias can also be intentionally introduced, leading to exclusion or discrimination based on specific characteristics. In some cases, biases are unintentional but can still result in exclusionary effects.

1. Is bias intentionally harmful/hateful?

Bias itself may not always be intentionally harmful or hateful. It can originate from historical or societal prejudices embedded in data. However, intentional actions to introduce bias with harmful or hateful intentions can occur, especially when biases are manipulated to perpetuate discrimination or disadvantage specific groups.

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

Effective software development involves receiving feedback from a diverse set of stakeholders, including users, developers, and other relevant parties. Engaging with a wide variety of people helps identify potential biases, usability issues, and ensures that the software meets the needs of a diverse user base. Feedback from different perspectives is crucial for creating inclusive and well-rounded applications.

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

Biases in applications like YouTube Kids can take various forms:

Content Bias: The recommendation algorithm may inadvertently favor certain types of content over others, leading to a lack of diversity in the content shown.
Cultural Bias: The platform may prioritize content that aligns with specific cultural norms or perspectives, excluding content from other cultures.
Gender Bias: The algorithm might unintentionally recommend content that aligns with gender stereotypes, limiting exposure to diverse perspectives.
Educational Bias: Certain educational content may be prioritized over others, impacting the variety of learning opportunities presented to children.
Language Bias: The platform may favor content in certain languages, limiting access to educational material and entertainment for speakers of other languages.

Addressing these biases is crucial to ensure that platforms like YouTube Kids provide a balanced and inclusive experience for all users, considering the diverse backgrounds and preferences of children and their families.