π Beneficial and Harmful Effects of Technology
5.1: Beneficial and Harmful Effects
What do YOU think is a beneficial effect?
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What do YOU think is a harmful effect?
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Today, our group will go over examples of Beneficial, Harmful, and Debatable aspects of technology.
β Beneficial Effects of Technology
What do you think is the most beneficial of these? Why?
Brainstorm additional beneficial effects of technology!
β οΈ Harmful Effects of Technology
π€ Neutral Effects: Beneficial and Harmful
- Benefits: Helps with assignments, analyzes data, boosts productivity, and supports healthcare.
- Harms: Environmental impact, potential misuse (deepfakes), and concerns over critical thinking in schools.
- Benefits: Useful in search & rescue, agriculture, and wildfire monitoring.
- Harms: Privacy concerns, crash risks, and ethical concerns in military use.
- Benefits: Potential to cure genetic diseases, improve crops, and boost food security.
- Harms: Can cause irreversible genetic harm, raise ethical issues, and may affect future generations.
Pick a dilemma from above. Do you think itβs more beneficial or harmful?
π Homework Hacks
Based on the brainstorming you did during the lesson, pick a beneficial or harmful effect and in 5 sentences fully explain why you think this topic is beneficial or harmful.
MCQ: Complete the multiple-choice questions linked below.
π§ What is Computing Bias?
Bias: An inclination or prejudice in favor of or against a person or group, typically in a way that is unfair.
Computing bias occurs when computer programs, algorithms, or systems produce results that unfairly favor or disadvantage certain groups. This bias can result from biased data, flawed design, or unintended consequences of programming.
π₯ Example: Netflix Recommendation Bias
- Majority Preference Bias: Recommending mostly popular content, making niche content harder to find.
- Filtering Bias: Limiting suggestions based on narrow viewing history.
π§ How Does Computing Bias Happen?
- Unrepresentative or Incomplete Data: Lacks diversity and skews results.
- Flawed or Biased Data: Reflects historical prejudices.
- Data Collection & Labeling: Human annotators may introduce personal/cultural bias.
π Explicit vs Implicit Data
Explicit Data: Data the user directly provides (e.g. name, age, preferences).
Implicit Data: Inferred from behavior (e.g. watch time, interactions).
What is an example of Explicit Data?
A) Viewing history
B) Name and age entered in account setup β
C) Time spent watching genres
π Types of Bias
- Algorithmic Bias: Systemic unfairness due to how algorithms operate.
- Data Bias: Biased or incomplete training data.
- Cognitive Bias: Personal beliefs shaping data selection (e.g. confirmation bias).
What is an example of Data Bias?
A) Hiring algorithm favors males.
B) Dataset underrepresents certain groups β
C) Researcher only selects supportive data.
π§ Intentional vs Unintentional Bias
- Intentional Bias: Algorithm is purposely designed to favor one group.
- Unintentional Bias: Happens unknowingly due to flawed data or processes.
Share a real-world biased scenario.
Classmates decide if itβs intentional or unintentional.
π Mitigation Strategies
- Pre-processing: Fix missing data, ensure diversity.
- In-processing: Balance datasets, validate fairness.
- Post-processing: Monitor real-world performance and adjust.