Data Privacy: Collecting, storing and using user data responsibly to respect individual rights. Bias and Fairness: Fixing biases in the data that can lead to discriminatory outcomes and perpetuate existing inequalities. Transparency: Making AI explainable so humans can understand and challenge algorRead more
- Data Privacy: Collecting, storing and using user data responsibly to respect individual rights.
- Bias and Fairness: Fixing biases in the data that can lead to discriminatory outcomes and perpetuate existing inequalities.
- Transparency: Making AI explainable so humans can understand and challenge algorithmic decisions.
- Accountability: Establishing clear accountability for AI systems and mechanisms for recourse when harm occurs.
- Informed Consent: Getting explicit consent from users when collecting and processing their data.
- Security: Protecting AI from cyber attacks that could compromise its integrity and lead to harm.
- Autonomy and Control: Balancing automation with human oversight so AI supports human decision making not replaces it.
- Social Impact: Considering the broader societal implications of AI deployment, including job displacement and economic inequality.
- Misuse: Preventing AI from being used for malicious purposes like deepfakes or autonomous weapons.
- Inclusivity: Having diverse representation in AI teams to fix biases and build more equitable systems.
Addressing these ethical considerations is crucial for developing AI algorithms that are fair, transparent, and beneficial to society.
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