Big Idea 5 - Computing Bias

📌 What is Computing Bias?

  • Bias: Prejudice for or against a group unfairly.
  • Computing Bias: When algorithms or computer systems favor or disadvantage groups unfairly.
  • Causes: Biased data, flawed design, unintended programming outcomes.

🎥 Example: Netflix Recommendation Bias

🔍 How Bias Can Occur:

  • Majority Preference Bias: Favors popular content, hides niche options.
  • Filtering Bias: Limits recommendations based on narrow user history.

🧐 How Does Computing Bias Happen?

  1. Unrepresentative or Incomplete Data: Lack of diversity = biased results.
  2. Flawed or Biased Data: Historical prejudice in data leads to biased output.
  3. Data Collection & Labeling: Human annotators may add bias unintentionally.

📊 Explicit Data vs. Implicit Data

  • Explicit Data:
    • Definition: Directly provided by user/programmer.
    • Example: Name, age, preferences, user ratings.
  • Implicit Data:
    • Definition: Inferred from behavior, not directly given.
    • Example: Viewing history, watch time.

⚖️ Implications:

  • Implicit Data: May reinforce past preferences, limit content diversity.
  • Explicit Data: More accurate but still prone to bias via input design.

🤔 Popcorn Hack #1

Question: What is an example of Explicit Data?

  • A) Netflix recommends shows based on your viewing history.
  • B) You provide your name, age, and preferences when creating a Netflix account.
  • C) Netflix tracks the time you spend watching certain genres.
  • Answer: B) You provide your name, age, and preferences when creating a Netflix account.

📝 Types of Bias

🤖 Algorithmic Bias

  • Definition: Bias from flawed algorithms.
  • Example: Amazon hiring algorithm favoring men due to biased past data.

📈 Data Bias

  • Definition: Bias in the dataset itself.
  • Example: Healthcare AI underestimating risk for underrepresented populations.

🧠 Cognitive Bias

  • Definition: Human bias during data collection.
  • Example: Researcher choosing data supporting their screen time belief (confirmation bias).

🤔 Popcorn Hack #2

Question: What is an example of Data Bias?

  • A) A hiring algorithm favors male candidates because the training data contains a disproportionate number of male resumes.
  • B) A system is trained on a dataset where certain groups, such as people with darker skin tones, are underrepresented.
  • C) A researcher intentionally selects data that supports their own beliefs about the impact of screen time on grades.
  • Answer: B) A system is trained on a dataset where certain groups, such as people with darker skin tones, are underrepresented.

Intentional Bias vs. Unintentional Bias

Intentional Bias:

  • Definition: Bias deliberately introduced.
  • Example: Algorithm prioritizing resumes with keywords