Introduction
Data science ethics is a critical field that focuses on the responsible development and deployment of data-driven solutions. It addresses the potential harms that can arise from the use of data, including bias, discrimination, privacy violations, and manipulation.
Key Concepts
- Bias in Data: Understanding how biases can be introduced into datasets and algorithms.
- Algorithmic Fairness: Developing algorithms that treat all individuals and groups equitably.
- Privacy & Data Security: Protecting sensitive data and ensuring compliance with privacy regulations (e.g., GDPR, CCPA).
- Transparency & Explainability: Making algorithms more understandable and accountable.