Understanding AI Bias
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Questions and Answers

What is AI bias?

  • Prejudice in data used to create AI algorithms leading to discrimination (correct)
  • The preference of AI algorithms for certain types of data
  • The speed at which AI algorithms process information
  • The accuracy of AI algorithms in decision-making

    • How can AI bias creep into algorithms?

  • By ensuring that all groups are equally represented in the training data
  • By excluding sensitive variables such as gender, race, or sexual orientation from training data
  • Through biased human decisions or reflecting historical inequities in training data (correct)
  • By using flawless data sampling techniques in training data

    • What was the issue with Amazon's hiring algorithm?

  • It favored applicants based on words more commonly found on women's resumes
  • It rejected qualified candidates based on irrelevant criteria
  • It was too slow in processing applications
  • It favored applicants based on words more commonly found on men's resumes (correct)

    • Why are virtual assistants predominantly given a female voice?

    <p>Historical bias and societal norms influenced this choice</p> Signup and view all the answers

    Is the assumption made by Google about salon searches an example of bias?

    <p>Yes, it reflects a bias assuming salon searches are mainly for females.</p> Signup and view all the answers

    Study Notes

    AI Bias

    • AI bias refers to the phenomenon where artificial intelligence systems perpetuate and amplify existing social biases, stereotypes, and prejudices.
    • AI bias can occur due to flawed or incomplete data, problematic algorithm design, or societal influences.

    Sources of AI Bias

    • AI bias can creep into algorithms through various means, including:
      • Biased training data, which reflects the systemic inequalities and biases present in society.
      • Algorithmic design flaws, which can unintentionally prioritize certain groups or characteristics.
      • Human biases, which can be introduced by developers and programmers.

    Real-World Examples of AI Bias

    • Amazon's hiring algorithm, which was designed to filter out unqualified candidates, was found to be biased against women, as it had been trained on predominantly male resumes.
    • Virtual assistants, such as Siri and Alexa, are often given a female voice, which can perpetuate gender stereotypes and reinforce societal expectations.

    Examples of Bias in Search Results

    • Google's search results for salons were found to be biased, as they would often display results that prioritized luxury salons over more affordable options, which can be a reflection of the company's business model and biases.
    • This bias can have real-world implications, such as excluding certain groups from accessing essential services or perpetuating socioeconomic inequalities.

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    Description

    Explore the concept of AI bias and its implications for AI algorithms. Learn about how biased data can lead to discrimination and social consequences in artificial intelligence.

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