Amazon's Online Recruiting Tool Bias

This section discusses the discovery of bias in Amazon's online recruiting tool, which favored male candidates due to the training data it was provided.

Bias in Amazon's Recruiting Tool

• Amazon's software engineers discovered a problem with their new online recruiting tool that showed a preference for male candidates.

• The glitch occurred because the computer models used to train the tool were based on patterns and resumes of job candidates over a 10-year period, which were predominantly from men.

• The tool learned to associate certain keywords related to women (e.g., "women's") with unsuccessful candidates, leading to biased results.

Examples of Biased Keywords

This section provides examples of keywords that triggered bias in Amazon's recruiting tool.

Examples of Biased Keywords

• The tool exhibited bias against resumes containing words like "women's," such as "captain of a women's chess club" or "captain of a women's soccer team."

• Even mentioning attending a women's university could trigger bias in the tool.

• These biases arose because Amazon had predominantly hired male engineers and software developers, resulting in skewed training data.

Disbanding the Recruiting Tool Unit

This section discusses how Amazon responded to the biased recruiting tool by disbanding the unit responsible for its creation.

Disbanding the Unit

• After discovering the biases in their online recruiting tool, Amazon decided not to solely rely on it for hiring decisions.

• By the start of last year, they disbanded the unit responsible for creating and maintaining the tool.

• It is important to note that Amazon never fully relied on this tool alone and used other methods for recruitment as well.

Limitations of Artificial Intelligence

This section highlights the limitations of artificial intelligence and the importance of providing unbiased data for training AI models.

Limitations of AI

• Amazon's response to the biased recruiting tool highlights the fact that artificial intelligence is only as smart as the information it is fed.

• The saying "garbage in, garbage out" applies to AI systems, meaning that if biased or flawed data is used for training, the system will reflect those biases.

• It is crucial to ensure that AI models are trained on diverse and unbiased datasets to avoid perpetuating discrimination or bias.

Automation in Recruitment

This section discusses the growing trend of automating recruitment processes and mentions companies like Hilton and Unilever using software made by hirevue for video-based applicant assessments.

Automation in Recruitment

• Many companies are turning to automation in their recruitment processes to make hiring faster and more standardized.

• Companies like Hilton and Unilever use software provided by hirevue, which allows applicants to record video responses to employers' questions.

• Hirevue's CEO claims that their software analyzes these video recordings to assess candidates.

Timestamps have been associated with bullet points where available.