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:
- Is bias enhancing or intentionally excluding? Intentially exlcuding as it is exluding information from other sources.
- Is bias intentionally harmful/hateful? Not always but most of the time yes.
- During software development are your receiving feedback from a wide variety of people? You should be in order to eliminate bias in what you are trying to create.
- What are the different biases you can find in an application such as Youtube Kids? I’ve seen some chinese communist propaganda on there but usaully the bias is toward lighthearted information
Answer in complete sentences, due Sunday 11:59 pm