Podcast
Questions and Answers
Which of the following is NOT a core ethical principle guiding data collection practices?
Which of the following is NOT a core ethical principle guiding data collection practices?
- Fairness
- Efficiency (correct)
- Transparency
- Privacy
Why is it important to collect data ethically for AI solutions?
Why is it important to collect data ethically for AI solutions?
- To reduce the cost of data storage.
- To ensure communities are well represented and biases are mitigated. (correct)
- To simplify the data analysis process.
- To speed up the data collection process.
What does fairness and bias reduction in data collection primarily aim to achieve?
What does fairness and bias reduction in data collection primarily aim to achieve?
- To ensure equal storage space for all data types.
- To accelerate the training of AI models.
- To increase the volume of data collected.
- To minimize bias and ensure fair representation in the data collected. (correct)
Why is transparency important in the data collection process?
Why is transparency important in the data collection process?
In the context of ethical AI data collection, what does 'accountability' primarily refer to?
In the context of ethical AI data collection, what does 'accountability' primarily refer to?
What is the primary reason for ensuring data accuracy and quality in AI data collection?
What is the primary reason for ensuring data accuracy and quality in AI data collection?
When planning data collection, what is the significance of obtaining informed consent?
When planning data collection, what is the significance of obtaining informed consent?
Which of the following actions best exemplifies 'using data responsibly' during AI data collection?
Which of the following actions best exemplifies 'using data responsibly' during AI data collection?
What is the purpose of creating a mechanism for feedback in the data collection process?
What is the purpose of creating a mechanism for feedback in the data collection process?
Which practice exemplifies ensuring data security during AI data collection?
Which practice exemplifies ensuring data security during AI data collection?
What action demonstrates accountability in an organization's AI data collection process?
What action demonstrates accountability in an organization's AI data collection process?
An AI model trained on biased data shows significantly lower accuracy for one ethnic group compared to others. Which ethical principle was most likely violated during data collection?
An AI model trained on biased data shows significantly lower accuracy for one ethnic group compared to others. Which ethical principle was most likely violated during data collection?
A data scientist discovers that the collected dataset includes sensitive personal information that was not mentioned in the informed consent. What is the most ethical course of action?
A data scientist discovers that the collected dataset includes sensitive personal information that was not mentioned in the informed consent. What is the most ethical course of action?
A research team is collecting facial recognition data, but to minimize direct association, they decide to represent each face through a high-dimensional vector generated by an autoencoder. While the individual identities are obscured, demographic biases present in the original dataset are still subtly encoded in the vector space. Which ethical consideration is most directly challenged by this scenario?
A research team is collecting facial recognition data, but to minimize direct association, they decide to represent each face through a high-dimensional vector generated by an autoencoder. While the individual identities are obscured, demographic biases present in the original dataset are still subtly encoded in the vector space. Which ethical consideration is most directly challenged by this scenario?
Researchers are developing an AI model to predict recidivism rates. They use historical criminal justice data that reflects existing societal biases, leading to a model that disproportionately flags individuals from minority communities as high-risk. The researchers argue that the model is simply reflecting real-world patterns. Which ethical stance is most challenged by this approach, and what would be needed to address it?
Researchers are developing an AI model to predict recidivism rates. They use historical criminal justice data that reflects existing societal biases, leading to a model that disproportionately flags individuals from minority communities as high-risk. The researchers argue that the model is simply reflecting real-world patterns. Which ethical stance is most challenged by this approach, and what would be needed to address it?
Flashcards
Fairness and Bias Reduction
Fairness and Bias Reduction
Collecting data in a way that minimizes bias and ensures fair presentation, preventing discrimination in AI models.
Privacy & Data Protection
Privacy & Data Protection
Protecting personal information during AI data collection, ensuring data is collected and used respecting individual privacy rights.
Transparency in Data Collection
Transparency in Data Collection
Ensuring the data collection process is transparent by clearly defining what data is collected, its purpose, how it will be used, and who has access.
Data Accuracy & Quality
Data Accuracy & Quality
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Data Security
Data Security
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Accountability in AI Data
Accountability in AI Data
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Informed Consent
Informed Consent
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Transparency & Accountability
Transparency & Accountability
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Responsible Data Use
Responsible Data Use
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Feedback Mechanisms
Feedback Mechanisms
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Data Accuracy
Data Accuracy
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Study Notes
- This unit examines the ethical principles that guide data collection practices and links them to concepts discussed in Micro Course 2
- Learners will explore the importance of fairness, accountability, transparency, and privacy (FATP) in data collection
- Outlined is how to apply these principles when planning data collection to ensure ethical and responsible practices
Key Ethical Principles to Data Collection
- Ethical data collection leads to better outcomes when building AI solutions, ensuring communities are well represented and biases are mitigated
Fairness and Bias Reduction
- Data should be collected in a way that minimizes bias and ensures fair presentation
- AI models trained on biased data can enforce discrimination, as seen with facial recognition misidentifying certain ethnic groups
Privacy & Data Protection
- AI data collection should protect personal information and ensure data is collected and used in a way that respects individuals' right to privacy
Transparency
- The data collection process should be transparent, defining what data is collected, the purpose of data, how it will be used, and who will have access
Data Accuracy & Quality
- Data should be accurate, complete, and up-to-date to ensure AI models make reliable predictions and decisions
- Inaccurate, poor-quality data can lead to inaccurate AI output
Security
- AI data collection should provide security against unauthorized access to protect sensitive information included in the dataset
Accountability
- Organizations are responsible for the ethical handling of AI data and the transparency of their processes
- Transparency ensures users trust the organization and the AI systems being used, particularly when personal data is involved
Applying Ethical Considerations When Planning Data Collection to Promote Responsible Practices
- Deliberately integrate ethics into your methodology when planning data collection
Informed Consent
- Inform people what their data will be used for and obtain their permission before collecting it
- Ensure people understand what they're agreeing to, and allow them to say "no" or leave at any time
Be Transparent & Accountable
- Be open about how and why data is being collected
- Explain goals clearly and take responsibility for how the data is handled
Use Data Responsibly
- Only use the data for the purposes shared with people, as explained in the consent form
- Avoid using the data in harmful ways
Get Feedback
- Create ways for people to share thoughts about the data collection process
- Make changes if necessary
Ensure Accuracy
- Collect data that is accurate and true
- Check for errors and ensure the data represents reality
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