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The ethical implications of AI have transitioned from theoretical discussions to urgent real-world issues. Francesca Rossi, IBM's Global Leader for AI Ethics, emphasizes the importance of trust, explainability, and transparency in AI systems. As AI becomes more integrated into daily life, addressing ethical challenges like data privacy and copyright protection is crucial.
Rossi outlines IBM's comprehensive framework for Responsible AI, which includes principles and practical implementations to guide AI development. She advocates for collaboration between companies and policymakers to create effective regulations that foster innovation while ensuring ethical standards. The conversation highlights the need for ongoing education and training in AI ethics, especially as India emerges as a key player in the global AI landscape.
• Trust in AI systems relies on explainability and transparency.
• IBM's AI ethics framework includes principles and practical implementations.
Responsible AI refers to the ethical framework guiding the design and deployment of AI systems.
AI explainability ensures that AI systems provide understandable and interpretable outputs to users.
Bias mitigation involves strategies to reduce unfair biases in AI models and their outcomes.
IBM is a leader in AI ethics, developing frameworks and tools to ensure responsible AI practices.
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