Understanding AI Bias

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.