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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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References
Chapter 12_ Ethics and Bias in AI.pdfClass Notes
Memorization
What we have learnt
Revision Tests
Term: Fairness in AI
Definition: The concept that AI systems should operate without discrimination against individuals or groups based on attributes such as race, gender, or age.
Term: Accountability
Definition: The establishment of clear responsibilities for AI decisions and their consequences, ensuring that developers and organizations can be held liable.
Term: Data Privacy
Definition: The protection of personal information used in AI development, necessitating measures like anonymization and compliance with regulations.
Term: Transparency
Definition: The openness of AI systems regarding their functionality and decision-making processes, fostering trust among users and stakeholders.