AI Ethics, Bias, and Responsible AI
The chapter outlines the ethical challenges associated with Artificial Intelligence, emphasizing the need for fair, accountable, and transparent AI systems. It discusses various types of bias, principles for responsible AI development, and the importance of governance frameworks. Ethical considerations in AI development are highlighted to ensure that technology serves humanity positively.
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Sections
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What we have learnt
- Ethical design is essential for trustworthy and inclusive AI systems.
- Bias can enter at any stage: data collection, labeling, modeling, deployment.
- FATE principles guide responsible AI development.
- Legal frameworks are evolving to regulate AI use.
- Privacy, security, and transparency are pillars of responsible AI.
Key Concepts
- -- Data Bias
- Skewed or incomplete data leading to underrepresentation of minority groups.
- -- Labeling Bias
- Subjective or inconsistent annotations made by human annotators that introduce personal biases into datasets.
- -- Algorithmic Bias
- Bias that is amplified due to optimization processes in modeling.
- -- FATE Principles
- Four key principles of ethical AI: Fairness, Accountability, Transparency, and Ethics.
- -- Differential Privacy
- A technique that adds noise to data to protect individual identities.
Additional Learning Materials
Supplementary resources to enhance your learning experience.