Introduction to Data Ethics

Study Notes

Data is a powerful tool used to drive decisions and impact society.
Data ethics guides businesses and professionals on how to ethically collect, store, and use data.
Data ethics considers the moral obligations when gathering, protecting, and using personally identifiable information.
Data ethics asks: "Is this the right thing to do?" and "Can we do better?"

Data ethics evaluates data practices (collecting, generating, analyzing, and disseminating data) impacting individuals and society.
It's concerned with right and wrong conduct, and also the use of transparency and defensibility of decisions made with automated/artificial intelligence (AI).
It builds upon and extends computer and information ethics, focusing on data-centric issues.
Data ethics focuses on individual data based on third-party practices, unlike media or journalism ethics.

A universal framework for data usage is needed.
There is a need for clearer guidelines, potentially with a more definitive (i.e., black and white) approach.
Protecting customer data privacy is a critical aspect. Data must remain private and confidential, and customers must be informed of data usage.
Individuals should have control over their data.

Trust: Companies demonstrating ethical use of data gain trust and loyalty.
Fair Practices: Avoiding bias in data decisions leads to fairer outcomes.
Data Privacy Compliance: Following regulations and ethical practices improves compliance with the likes of GDPR and CCPA.

Transparency is critical. Customers must be informed clearly about how their data is used.
Control: Customers need full control over their data.
Data points that may be unnecessary: Not asking for data that does not relate to the particular issue at hand.

Ownership: Individuals own their personal information and must consent to data collection
Transparency: Individuals have the right to know how their data is used.
Privacy: Privacy must be protected even with consent. Personally Identifiable Information (PII) should be handled with care and stored securely.
Intention: Data collection should have a positive purpose.
Outcomes: Consider the potential impacts of data analysis on individuals or groups.

Algorithms handling sensitive information must be ethical.
Decisions made by algorithms should be transparent.
Bias and discrimination in algorithms require careful consideration to eliminate biases in decisions.
Ensuring privacy and data security is critical when using algorithms.
Companies must have an ethical framework for designing and deploying algorithms.

AI Review Boards are responsible for ensuring ethical use of AI.
Companies should establish a formal code of ethics to guide their AI implementation.
Audit trails are essential for verifying algorithm use and processes, which can help maintain transparency at all stages.