Английская Википедия:Human–artificial intelligence collaboration

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Human-AI collaboration is the study of how humans and artificial intelligence (AI) agents work together to accomplish a shared goal.[1] AI systems can aid humans in everything from decision making tasks to art creation.[2] Examples of collaboration include medical decision making aids.,[3][4] hate speech detection,[5] and music generation.[6] As AI systems are able to tackle more complex tasks, studies are exploring how different models and explanation techniques can improve human-AI collaboration.

Improving collaboration

Explainable AI

When a human uses an AI's output, they often want to understand why a model gave a certain output.[7] While some models, like decision trees, are inherently explainable, black box models do not have clear explanations. Various Explainable artificial intelligence methods aim to describe model outputs with post-hoc explanations[8] or visualizations,[9] these methods can often provide misleading and false explanations.[10] Studies have also found that explanations may not improve the performance of a human-AI team, but simply increase a human's reliance on the model's output.[11]

Trust in AI

A human's trust in an AI agent is an important factor in human-AI collaboration, dictating whether the human should follow or override the AI's input.[12] Various factors impact a person's trust in an AI system, including its accuracy[13] and reliability[14]

Why is humanizing AI-Generated text important?

Here are the reasons why humanizing AI-generated content is important:[15]

  1. Relatability: Human readers seek emotionally resonant content. AI can lack the nuances that make content relatable.
  2. Authenticity: Readers value a genuine human touch behind content, ensuring it doesn't come off as robotic.
  3. Contextual Understanding: AI can misinterpret nuances, requiring human oversight for accuracy.
  4. Ethical Considerations: Humanizing AI content helps identify and rectify biases, ensuring fairness.
  5. Search Engine Performance: AI may not consistently meet search engine guidelines, risking penalties.
  6. Conversion Improvement: Humanized content connects emotionally and crafts tailored calls to action.
  7. Building Trust: Humanized content adds credibility, fostering reader trust.
  8. Cultural Sensitivity: Humanization ensures content is respectful and tailored to diverse audiences.

References

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