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Ethical considerations in machine learning.

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In this video, we will understand why ethics is important in machine learning.

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Identify key ethical issues such as bias, privacy, and transparency.

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Describe the impact of ML on jobs and the importance of accountability.

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Why ethics in ML?

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Machine learning is powerful, but it must be used responsibly.

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Decisions made by models can affect people's education, jobs, and privacy.

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Ethics ensures models are fair, transparent, and accountable.

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Bias and fairness.

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Bias occurs when training data is unbalanced or unfair.

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If models are trained on limited data, they may favor one group over another.

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This can lead to unjust results in hiring, loans, or health care.

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Data bias, training only on one group's data.

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Impact, wrong predictions for underrepresented groups.

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Privacy and transparency.

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ML systems often use sensitive information like health or financial data.

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If not handled properly, this can cause serious privacy risks.

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Anonymization. Hide personal details.

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Consent. Always get permission before collecting data.

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Impact and responsibility.

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AI is changing how people work and raising new challenges.

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Some jobs may disappear while others will evolve with AI tools.

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At the same time, accountability must remain with humans, not machines.

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Job replacement. Automation takes over some tasks.

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Job transformation. AI makes people's work more efficient.

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Responsibility. Developers and organizations are accountable for mistakes.

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Ethical considerations in machine learning ensure models are fair,

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protect privacy, and remain transparent.

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By holding humans accountable and preparing for job changes,

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we can use AI responsibly for the benefit of all.