📚 Topic Summary
Bias in algorithms arises when the data used to train the algorithm reflects existing societal biases, leading the algorithm to make unfair or discriminatory decisions. Understanding the sources of bias, such as historical data, sampling bias, and measurement bias, is crucial for developing fair and ethical AI systems. Multiple-choice questions can help you practice identifying and mitigating these biases.
This worksheet provides a practical approach to recognizing and addressing algorithmic bias through vocabulary exercises, fill-in-the-blank questions, and critical thinking prompts. By working through these activities, you will gain a deeper understanding of how bias can creep into algorithms and how to design systems that are more equitable and just.
🧠 Part A: Vocabulary
Match the following terms with their definitions:
| Term |
Definition |
| 1. Algorithmic Bias |
A. Data collected does not accurately represent the population. |
| 2. Data Bias |
B. Unintended prejudice in an algorithm's output. |
| 3. Sampling Bias |
C. Using biased data to train an algorithm. |
| 4. Prejudice |
D. The underlying unfairness present in the dataset itself |
| 5. Feedback Loop |
E. Preconceived judgements or opinions |
Answer Key:
| Term |
Correct Definition |
| 1. Algorithmic Bias |
B |
| 2. Data Bias |
D |
| 3. Sampling Bias |
A |
| 4. Prejudice |
E |
| 5. Feedback Loop |
C |
✍️ Part B: Fill in the Blanks
Complete the following sentences using the words provided below:
( Fairness, Training Data, Discrimination, Mitigation, Transparency )
- Ensuring ___________ in algorithms is crucial for building trust and accountability.
- The quality of ___________ significantly impacts the presence of bias in algorithms.
- __________ techniques are employed to reduce the impact of algorithmic bias.
- Algorithmic bias can lead to ___________ against certain groups of people.
- Achieving ___________ in algorithmic outcomes requires careful consideration of ethical implications.
Answer Key:
- Ensuring Transparency in algorithms is crucial for building trust and accountability.
- The quality of Training Data significantly impacts the presence of bias in algorithms.
- Mitigation techniques are employed to reduce the impact of algorithmic bias.
- Algorithmic bias can lead to Discrimination against certain groups of people.
- Achieving Fairness in algorithmic outcomes requires careful consideration of ethical implications.
🤔 Part C: Critical Thinking
Describe a real-world scenario where algorithmic bias could have significant negative consequences, and suggest strategies to mitigate the risk of bias in that scenario.