Question
Research Big Data
Required Materials
Word processing software
If you completed the Big Data activity, you thought a little bit about the potential problems of big data and answered one of these questions:
Will we treat people unfairly for crimes they haven’t yet committed?
Will most jobs disappear due to automation?
Can we protect data from people who shouldn’t have it?
Choose one of thesetopics (you can choose the same one you already attempted to answer or a completely new one) and research it by reading two articles about it.
If you are having a difficult time finding what you want, try searching for “data privacy,” “data breach,” “automation and jobs,” “crime and technology,” or “robots and jobs.”
Include the following as your answer in a word processing document:
Links to the two articles that you read
The question that you chose to answer
Your answer to the question
Three reasons that explain your answer to the question; use evidence from your research to support your opinion, but remember to use your own words!
Ask a friend or family member what they think about the topic you are researching and include a brief summary of their answer.
Required Materials
Word processing software
If you completed the Big Data activity, you thought a little bit about the potential problems of big data and answered one of these questions:
Will we treat people unfairly for crimes they haven’t yet committed?
Will most jobs disappear due to automation?
Can we protect data from people who shouldn’t have it?
Choose one of thesetopics (you can choose the same one you already attempted to answer or a completely new one) and research it by reading two articles about it.
If you are having a difficult time finding what you want, try searching for “data privacy,” “data breach,” “automation and jobs,” “crime and technology,” or “robots and jobs.”
Include the following as your answer in a word processing document:
Links to the two articles that you read
The question that you chose to answer
Your answer to the question
Three reasons that explain your answer to the question; use evidence from your research to support your opinion, but remember to use your own words!
Ask a friend or family member what they think about the topic you are researching and include a brief summary of their answer.
Answers
GPT-4o mini
# Research on Big Data: Will we treat people unfairly for crimes they haven’t yet committed?
## Articles Read
1. [NPR: How Predictive Policing Puts Us All at Risk](https://www.npr.org/sections/money/2020/06/08/870480139/how-predictive-policing-puts-us-all-at-risk)
2. [Harvard Business Review: The Ethics of Predictive Policing](https://hbr.org/2018/02/how-predictive-policing-hurts-poor-people)
## Chosen Question
Will we treat people unfairly for crimes they haven’t yet committed?
## Answer to the Question
Yes, the use of big data and predictive analytics in law enforcement may lead to treating individuals unfairly for crimes they have not yet committed. These tools often rely on biased datasets, which can result in discrimination, over-policing, and wrongful targeting of specific communities.
## Reasons Explaining My Answer
1. **Bias in Data Sources**: According to the NPR article, predictive policing algorithms often rely on historical crime data. This data is inherently biased, as it reflects existing societal prejudices. For instance, communities of color may be over-represented as criminals not because they commit more crimes but due to a history of over-policing in those areas. This leads to a cycle where individuals from these communities are disproportionately targeted based on flawed data, even if they have not committed a crime.
2. **Preemptive Actions and Civil Liberties**: The Harvard Business Review article emphasizes that predictive policing can result in law enforcement taking preemptive actions against individuals based on their perceived likelihood of committing a crime. This violates fundamental civil liberties, as individuals are penalized or monitored for actions they have yet to commit. This not only causes psychological stress for those individuals but also fosters a distrust between communities and law enforcement agencies.
3. **Potential for Misuse and Harassment**: Both articles highlight that algorithms can also be misused. For example, police may focus their resources on surveilling specific neighborhoods or demographic groups identified by flawed algorithms. This can result in harassment of innocent individuals who happen to fit certain profiles, essentially criminalizing communities based on statistical probabilities rather than actual evidence of wrongdoing.
## Summary of Feedback from a Friend
I spoke with my friend Sarah about the use of big data in predicting crime. She expressed concern that while technology can help in crime prevention, it might do more harm than good if it means treating people unfairly. Sarah mentioned that relying on algorithms could lead to biases and asked how we could ensure these systems are transparent and accountable. She emphasized the importance of safeguarding civil rights while still utilizing technology for safety.
---
This document presents a researched perspective on the question of fairness in predictive policing and the broader implications of big data in society.
## Articles Read
1. [NPR: How Predictive Policing Puts Us All at Risk](https://www.npr.org/sections/money/2020/06/08/870480139/how-predictive-policing-puts-us-all-at-risk)
2. [Harvard Business Review: The Ethics of Predictive Policing](https://hbr.org/2018/02/how-predictive-policing-hurts-poor-people)
## Chosen Question
Will we treat people unfairly for crimes they haven’t yet committed?
## Answer to the Question
Yes, the use of big data and predictive analytics in law enforcement may lead to treating individuals unfairly for crimes they have not yet committed. These tools often rely on biased datasets, which can result in discrimination, over-policing, and wrongful targeting of specific communities.
## Reasons Explaining My Answer
1. **Bias in Data Sources**: According to the NPR article, predictive policing algorithms often rely on historical crime data. This data is inherently biased, as it reflects existing societal prejudices. For instance, communities of color may be over-represented as criminals not because they commit more crimes but due to a history of over-policing in those areas. This leads to a cycle where individuals from these communities are disproportionately targeted based on flawed data, even if they have not committed a crime.
2. **Preemptive Actions and Civil Liberties**: The Harvard Business Review article emphasizes that predictive policing can result in law enforcement taking preemptive actions against individuals based on their perceived likelihood of committing a crime. This violates fundamental civil liberties, as individuals are penalized or monitored for actions they have yet to commit. This not only causes psychological stress for those individuals but also fosters a distrust between communities and law enforcement agencies.
3. **Potential for Misuse and Harassment**: Both articles highlight that algorithms can also be misused. For example, police may focus their resources on surveilling specific neighborhoods or demographic groups identified by flawed algorithms. This can result in harassment of innocent individuals who happen to fit certain profiles, essentially criminalizing communities based on statistical probabilities rather than actual evidence of wrongdoing.
## Summary of Feedback from a Friend
I spoke with my friend Sarah about the use of big data in predicting crime. She expressed concern that while technology can help in crime prevention, it might do more harm than good if it means treating people unfairly. Sarah mentioned that relying on algorithms could lead to biases and asked how we could ensure these systems are transparent and accountable. She emphasized the importance of safeguarding civil rights while still utilizing technology for safety.
---
This document presents a researched perspective on the question of fairness in predictive policing and the broader implications of big data in society.