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4.3.2.2 Data Ethics Considerations - Coggle Diagram
4.3.2.2 Data Ethics Considerations
Data Ethics
Data ethics
Study of moral challenges in data handling to ensure ethical solutions in data use.
Key goals
Compliance, trustworthiness, fair data use, bias minimization, and positive public perception.
Data Privacy
Definition
Proper handling of
data collection,
processing,
sharing
individual rights
to privacy
Increase privacy awareness
Educate on data security and privacy protocols for:
team members,
vendors
stakeholders
Use security tools
Leverage
encrypted storage
password managers
restrictive privacy settings
Data anonymization
Protect PII (e.g., names, SSNs, account numbers) with techniques like:
blanking
hashing
masking.
Data Bias
Data bias
Errors that skew data in a specific direction.
often through unrepresentative sampling or data handling
Types of Bias
Sampling bias
Results from unrepresentative samples
excluding specific demographic groups.
Observer bias
Different stakeholders interpret data differently based on their perspectives.
Interpretation bias
Viewing inconclusive data in a consistently positive or negative light.
Confirmation bias
Seeking data that aligns with pre-existing beliefs or preferences.
Key Takeaway
Ethical decision-making
Critical for reducing risk, increasing trust, ensuring long-term success, and building a positive reputation
Leadership in data ethics
Requires prioritizing privacy and addressing biases to ethically present a project’s data-informed story.