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AI Challenges
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AI Challenges

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Questions and Answers

What is one of the concerns related to AI systems and large datasets?

  • Data Privacy and Security Concerns (correct)
  • Algorithmic Bias and Discrimination
  • High Environmental Impact
  • Lack of Transparency and Explainability
  • What is one of the reasons AI models, especially deep learning, operate as 'black boxes'?

  • Inadequate training
  • Overcomplicated algorithms
  • Unclear decision-making (correct)
  • Lack of data
  • What is a consequence of AI automation?

  • Economic growth
  • Environmental degradation
  • Job creation
  • Job displacement (correct)
  • What is one of the reasons AI systems perpetuate biases?

    <p>Biased training data</p> Signup and view all the answers

    What is one of the environmentally friendly approaches to AI development?

    <p>Reducing energy consumption</p> Signup and view all the answers

    Study Notes

    Top 5 Challenges of Using AI

    • Data Privacy and Security Concerns: AI systems rely on large datasets, including personal and sensitive information, raising ethical issues around consent, data collection practices, and preventing breaches.
    • Strict data governance, audits for bias/fairness, and robust cybersecurity are crucial to address these concerns.

    Lack of Transparency and Explainability

    • Black Box Models: Many AI models, especially deep learning, operate as "black boxes" with unclear decision-making processes.
    • This hinders accountability, scalability, and adoption across critical domains like healthcare.
    • Explainable AI techniques are necessary for trustworthy and ethical AI systems.

    Algorithmic Bias and Discrimination

    • Societal Biases Amplified: AI algorithms can perpetuate and amplify societal biases present in training data.
    • This leads to unfair and discriminatory outcomes, especially for underrepresented groups.
    • Promoting diversity, inclusive data, and debiasing techniques is essential to address these biases.

    Environmental Impact

    • High Energy Consumption: Training large AI models requires immense computational power and energy consumption.
    • This contributes to e-waste generation from rapid hardware upgrades and shorter device lifespans.
    • Green AI strategies, optimizations, and sustainable practices are necessary to mitigate the environmental impact.

    Workforce Disruption and Economic Inequality

    • Job Displacement Risks: AI automation poses risks of job displacement and exacerbating economic disparities.
    • Reskilling initiatives and policies to address socioeconomic impacts are required.
    • Balanced human-AI collaboration is key to mitigating workforce disruption.

    Addressing AI Challenges

    • Solving AI challenges requires a collaborative effort from experts across various fields.
    • This involves creating new tech solutions, setting regulations, making ethical decisions, and ensuring global cooperation to make AI safe and fair for everyone.

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    Test your knowledge on the top challenges of using AI, including data privacy, lack of transparency, and more. Learn about the ethical issues and importance of strict data governance.

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