CBSE Class 11th AI (Artificial Intelligence) | 14. Ethics and Bias in AI by Abraham | Learn Smarter
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Knowledge
14. Ethics and Bias in AI

The chapter addresses the critical issues of ethics and bias in artificial intelligence (AI), emphasizing the necessity for ethical guidelines to ensure AI serves humanity fairly. It outlines various ethical concerns associated with AI technologies, the types and sources of bias that can impact AI outcomes, and highlights the importance of transparency, accountability, and inclusivity in AI development. Additionally, the chapter discusses practical measures for mitigating bias, illustrating these concepts with case studies and advocating for stronger regulations and societal awareness around ethical AI use.

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Sections

  • 14

    Ethics And Bias In Ai

    This section discusses the critical importance of ethics and bias in the development and application of Artificial Intelligence technologies.

    Learning Practice

  • 14.1

    Need For Ethics In Ai

    Ethics in AI is crucial for ensuring AI technologies are developed and used responsibly for the benefit of everyone.

    Learning Practice

  • 14.2

    Ethical Issues In Ai

    This section discusses ethical issues surrounding AI, focusing on privacy, job displacement, autonomous weapons, decision-making without human oversight, and misinformation.

    Learning Practice

  • 14.2.a

    Privacy And Surveillance

    The section discusses the ethical implications of privacy and surveillance in AI, highlighting concerns about data misuse and lack of consent.

    Learning Practice

  • 14.2.b

    Job Displacement

    Job displacement arises from automation and the implementation of AI technologies, leading to unemployment in various sectors.

    Learning Practice

  • 14.2.c

    Autonomous Weapons

    The section discusses the ethical dilemmas associated with autonomous weapons, particularly concerning accountability and the consequences of AI making life-and-death decisions.

    Learning Practice

  • 14.2.d

    Decision-Making Without Human Oversight

    This section discusses the ethical implications of AI systems making decisions autonomously, focusing on accountability and human oversight.

    Learning Practice

  • 14.2.e

    Deepfakes And Misinformation

    This section discusses the challenges posed by deepfakes and misinformation generated by AI, which can mislead the public and harm reputations.

    Learning Practice

  • 14.3

    Bias In Ai

    Bias in AI refers to systematic errors or unfair outcomes produced by AI systems, which can arise from data, algorithms, or societal prejudices.

    Learning Practice

  • 14.3.a

    Data Bias

    Data bias in AI occurs when machine learning algorithms produce unfair or skewed outputs due to the data used for training.

    Learning Practice

  • 14.3.b

    Algorithmic Bias

    Algorithmic bias refers to unfair outcomes produced by AI systems due to flawed algorithms or data.

    Learning Practice

  • 14.3.c

    Societal Bias

    Societal bias in AI reflects existing prejudices in society, which can lead to unethical behaviors in AI systems.

    Learning Practice

  • 14.4

    Sources Of Bias

    Bias in AI systems arises from various sources, influencing their fairness and reliability.

    Learning Practice

  • 14.5

    Impact Of Bias In Ai

    Biased AI can lead to discrimination, loss of trust, and legal violations.

    Learning Practice

  • 14.6

    Eliminating Bias In Ai

    This section discusses various strategies to eliminate bias in AI systems, including the use of diverse datasets, regular audits, human oversight, algorithm transparency, and ethical guidelines.

    Learning Practice

  • 14.6.a

    Diverse And Inclusive Datasets

    Diverse and inclusive datasets ensure fairness in AI by representing various demographics.

    Learning Practice

  • 14.6.b

    Regular Audits And Testing

    This section emphasizes the importance of regular audits and testing in mitigating biases within AI systems to ensure ethical outcomes.

    Learning Practice

  • 14.6.c

    Human Oversight

    Human oversight in AI is critical for ensuring responsible decision-making and accountability in systems that significantly impact human lives.

    Learning Practice

  • 14.6.d

    Algorithm Transparency

    Algorithm transparency refers to the clarity and openness in how AI systems make decisions, crucial for ensuring ethical use of AI.

    Learning Practice

  • 14.6.e

    Ethical Guidelines And Policies

    This section discusses the importance of establishing ethical guidelines and policies for AI to ensure its fair and responsible development.

    Learning Practice

  • 14.7

    Guidelines For Ethical Use Of Ai

    This section outlines key guidelines for promoting the ethical use of AI, focusing on fairness, accountability, transparency, a human-centric approach, and sustainability.

    Learning Practice

  • 14.8

    Case Studies And Examples

    This section presents notable case studies and examples that illustrate the ethical challenges and biases present in AI systems.

    Learning Practice

  • 14.8.a

    Amazon Recruitment Ai Tool

    The Amazon Recruitment AI Tool faced criticism for biases against women, stemming from training data primarily composed of male candidates.

    Learning Practice

  • 14.8.b

    Compas Algorithm In U.s. Court System

    The COMPAS algorithm is a predictive tool used in the U.S. court system to assess the likelihood of criminal reoffending, highlighting crucial issues of bias against minority groups.

    Learning Practice

  • 14.8.c

    Facial Recognition Systems

    Facial recognition systems exhibit significant racial biases, raising ethical concerns, especially in law enforcement.

    Learning Practice

  • 14.9

    Role Of Government And Society

    The government and society are crucial in shaping ethical AI through regulations, education, and collaborative efforts.

    Learning Practice

References

Chapter_14_Ethic.pdf

Class Notes

Memorization

What we have learnt

  • Ethics in AI is essential f...
  • Bias in AI can lead to disc...
  • Efforts to eliminate bias m...

Final Test

Revision Tests