Ethics and Bias in AI
Ethical considerations and the potential for bias are critical as Artificial Intelligence systems become prevalent in society. Responsible AI development seeks fairness, transparency, and accountability while maximizing societal benefits and minimizing harms. It is essential to navigate the complex challenges of fairness, data privacy, and security to foster trust and social responsibility in AI systems.
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What we have learnt
- Ethical considerations are fundamental to AI development.
- Fairness in AI requires mitigating biases in decision-making.
- Data privacy and security are essential in protecting personal information.
Key Concepts
- -- Fairness in AI
- The concept that AI systems should operate without discrimination against individuals or groups based on attributes such as race, gender, or age.
- -- Accountability
- The establishment of clear responsibilities for AI decisions and their consequences, ensuring that developers and organizations can be held liable.
- -- Data Privacy
- The protection of personal information used in AI development, necessitating measures like anonymization and compliance with regulations.
- -- Transparency
- The openness of AI systems regarding their functionality and decision-making processes, fostering trust among users and stakeholders.
Additional Learning Materials
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