📌 Popcorn Hacks - Computing Bias
🤔 Popcorn Hack #1
Question: What is an example of Explicit Data?
Options:
- 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.
This is explicit data because the user directly provides this information.
🤔 Popcorn Hack #2
Question: What is an example of Data Bias?
Options:
- 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.
This is data bias because the data itself is incomplete and does not reflect the full diversity of the population.
🤔 Popcorn Hack #3
Question: What is an example of Unintentional Bias?
Example: A facial recognition algorithm works better for lighter-skinned individuals because the training dataset mainly consists of images of people with lighter skin tones.
✅ Answer:
This is an example of Unintentional Bias because the developers did not deliberately exclude certain groups — the bias emerged from an unbalanced dataset. 🍪🍪
Homework Hack
✍️ Short-Answer Question
Explain the difference between implicit and explicit data. Provide an example of each.
Answer:
Explicit data is information that is directly provided by the user. It includes personal details or preferences that the user inputs manually.
Example: When creating a Netflix account, you provide your name, age, and preferred genres — this is explicit data because it is directly given by the user.
Implicit data is information inferred from a user’s actions or behavior rather than directly provided.
Example: Netflix tracking your viewing history and recommending shows based on your past activity is implicit data because it is gathered from your behavior rather than direct input.
📝 Lesson Notes:
- The blog explains computing bias and how algorithms can create or reinforce bias through flawed data, design, or unintended consequences.
- It covers three types of bias:
- Algorithmic Bias – Bias from a flawed or incomplete algorithm.
- Data Bias – Bias caused by unrepresentative or erroneous data.
- Cognitive Bias – Bias introduced by the researcher’s or developer’s own assumptions.
- The blog also explains explicit vs implicit data:
- Explicit Data – Directly provided by the user (e.g., name, preferences).
- Implicit Data – Inferred from user behavior (e.g., viewing history).
- The section on Intentional vs Unintentional Bias highlights that even if bias is not deliberate, it can still lead to unfair outcomes.
- The Mitigation Strategies section outlines how to reduce bias during:
- Pre-processing – Clean and diversify training data.
- In-processing – Use balanced datasets and synthetic samples.
- Post-processing – Monitor performance and adjust for fairness.