COM 250 EXAM 3 UPDATED QUESTIONS AND
CORRECT ANSWERS
Question:
1. Big Data
Answer:
1. Machines need lots of data/input to work well, and 2. That there is a vast amount of data about us to
collect, mostly online
Question:
2. Data
Answer:
A series of observations, measurements, or facts collected for analysis, or calculations
Question:
3. Data, Algorithms, and Models
Answer:
Data is the input, algorithms are the rules/processes applied to that data, and models composed of many
algorithms; are the output patterns built from running data through algorithms.
Question:
4. Supervised Machine Learning
Answer:
Humans tell the machine what the data are and which patterns to follow; then, machine identifies patterns
and solves data questions/problems
Question:
5. Unsupervised Machine Learning
Answer:
Humans give machine "unlabeled" data; machine acts to figure out what is going on in the data; then,
machine solves problems and draws conclusions (and presumably gets better by "learning" with more data)
Question:
6. Predictive AI
Answer:
Predictive AI forecasts an outcome based on past data (this past data is bais and incomplete) decision
making
Question:
7. Generative AI
Answer:
Generative AI creates new content that does not previously exist. sorting and classifying- creation
Question:
8. Mechanical Objectivity
, Answer:
The belief that machines and data can produce neutral, bias-free knowledge without human influence and
error.
Question:
9. Reasons Predictive AI Fails
Answer:
1. A good prediction can result in a bad decision
2. People can strategically game opaque AI.
3. Users over-rely on AI without adequate oversight or recourse
4. Data for training AI may come from a different population than the one it is used on
5. Predictive AI can increase inequalities.
Question:
10. Representational Harms of AI
Answer:
1. AI systems can deny people the opportunity to self-identify
2. They can reinforce social groups (characterizing social groups based on old data)
3. They can traffic in stereotypes (coming from the data people feed into the algorithms)
4. They can demean social groups
5. They can erase them entirely
Question:
11. Symbolic Annihilation
Answer:
The erasure or underrepresentation of groups in media or datasets, causing them to seem unimportant or
nonexistent. Not reflecting our reality.
Question:
12. AI Slop
Answer:
Low-quality, mass-generated AI content circulated online that lacks originality and drowns out
human-made work. associated with generative ai
Question:
13. Hallucinations (in AI)
Answer:
When AI produces information that has outright errors and inaccuracies associated with generative ai
Question:
14. Aspirational Labor
Answer:
Unpaid independent work driven by the prospect of future success in your passion
Question:
15. Aspirational Narratives in the Influencer Industry
CORRECT ANSWERS
Question:
1. Big Data
Answer:
1. Machines need lots of data/input to work well, and 2. That there is a vast amount of data about us to
collect, mostly online
Question:
2. Data
Answer:
A series of observations, measurements, or facts collected for analysis, or calculations
Question:
3. Data, Algorithms, and Models
Answer:
Data is the input, algorithms are the rules/processes applied to that data, and models composed of many
algorithms; are the output patterns built from running data through algorithms.
Question:
4. Supervised Machine Learning
Answer:
Humans tell the machine what the data are and which patterns to follow; then, machine identifies patterns
and solves data questions/problems
Question:
5. Unsupervised Machine Learning
Answer:
Humans give machine "unlabeled" data; machine acts to figure out what is going on in the data; then,
machine solves problems and draws conclusions (and presumably gets better by "learning" with more data)
Question:
6. Predictive AI
Answer:
Predictive AI forecasts an outcome based on past data (this past data is bais and incomplete) decision
making
Question:
7. Generative AI
Answer:
Generative AI creates new content that does not previously exist. sorting and classifying- creation
Question:
8. Mechanical Objectivity
, Answer:
The belief that machines and data can produce neutral, bias-free knowledge without human influence and
error.
Question:
9. Reasons Predictive AI Fails
Answer:
1. A good prediction can result in a bad decision
2. People can strategically game opaque AI.
3. Users over-rely on AI without adequate oversight or recourse
4. Data for training AI may come from a different population than the one it is used on
5. Predictive AI can increase inequalities.
Question:
10. Representational Harms of AI
Answer:
1. AI systems can deny people the opportunity to self-identify
2. They can reinforce social groups (characterizing social groups based on old data)
3. They can traffic in stereotypes (coming from the data people feed into the algorithms)
4. They can demean social groups
5. They can erase them entirely
Question:
11. Symbolic Annihilation
Answer:
The erasure or underrepresentation of groups in media or datasets, causing them to seem unimportant or
nonexistent. Not reflecting our reality.
Question:
12. AI Slop
Answer:
Low-quality, mass-generated AI content circulated online that lacks originality and drowns out
human-made work. associated with generative ai
Question:
13. Hallucinations (in AI)
Answer:
When AI produces information that has outright errors and inaccuracies associated with generative ai
Question:
14. Aspirational Labor
Answer:
Unpaid independent work driven by the prospect of future success in your passion
Question:
15. Aspirational Narratives in the Influencer Industry
