Generative AI

11.

Ethical Considerations and Responsible AI

11.1.

Bias and Fairness

11.1.1.

Sources of Bias

11.1.1.1.

Training Data Bias

11.1.1.2.

Algorithmic Bias

11.1.1.3.

Evaluation Bias

11.1.2.

Bias Mitigation

11.1.2.1.

Data Balancing Techniques

11.1.2.2.

Algorithmic Debiasing

11.1.2.3.

Post-Processing Corrections

11.1.3.

Fairness Metrics

11.1.3.1.

Demographic Parity

11.1.3.2.

11.1.3.3.

Individual Fairness

11.2.

Content Safety and Harmful Outputs

11.2.1.

Harmful Content Detection

11.2.1.1.

Toxicity Classification

11.2.1.2.

Hate Speech Detection

11.2.1.3.

Violence and Explicit Content

11.2.2.

Content Filtering

11.2.2.1.

Input Filtering

11.2.2.2.

Output Filtering

11.2.2.3.

Real-Time Moderation

11.2.3.

Safety Alignment

11.2.3.1.

Constitutional AI

11.2.3.2.

Red Team Testing

11.2.3.3.

Safety Fine-Tuning

11.3.

Misinformation and Deepfakes

11.3.1.

Synthetic Media Detection

11.3.1.1.

Deepfake Detection

11.3.1.2.

AI-Generated Text Detection

11.3.1.3.

Watermarking Techniques

11.3.2.

Provenance and Attribution

11.3.2.1.

Content Authentication

11.3.2.2.

Blockchain Verification

11.3.2.3.

Digital Signatures

11.4.

Privacy and Security

11.4.1.

11.4.1.1.

Differential Privacy

11.4.1.2.

Federated Learning

11.4.1.3.

Synthetic Data for Privacy

11.4.2.

11.4.2.1.

Adversarial Attacks

11.4.2.2.

Model Extraction

11.4.2.3.

Membership Inference

11.4.3.

Secure Deployment

11.4.3.1.

Encrypted Inference

11.4.3.2.

Secure Multi-Party Computation

11.4.3.3.

Privacy-Preserving APIs

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