AI Gender Bias: Global Outrage & HR Leaders’ Viewpoint


👉Incomplete or Incomprehensive Training Datasets

Diverse demographic categories missing from training datasets could be a huge factor in AI-based machines behaving in a gender-biased way. Models produced out of such datasets will fail to scale appropriately in the event of applying them to the new data full of the same missing categories. For example, if female orators contribute to a mere 10% of the training data. In that case, when one will apply a trained ML model to females, it will produce a larger number of errors.

👉Labels Applied to Machine-Learning Models

Almost all commercial AI machines take the assistance of supervised machine learning, meaning the training datasets are labeled to make AI systems learn to behave under different situations and circumstances. And mostly, these labels emerge out of the humans developing the AI algorithms. This way, the 🔗biases get encoded into ML models.

👉Dated ML Modeling Practices

Inputs fed to ML models can infuse biases into building algorithms. For example, for decades, field speech synthesis (an alternate name to NLP) — which comprises speech-to-text and text-to-speech technology had been performing poorly for females in comparison to males. The underlying reason lies in the fact that speech is being analyzed basis a taller speaker equipped with long vocal cords and a low-pitched voice.

  • Being dead-assured about the engineers and technicians involved in labeling audio samples are coming from different backgrounds (culture, race, color, sex, etc.).
  • 🔗ML algorithm developers must ensure measuring accuracy standards separately for diverse demographic categories. This will help negate biases as there will exist no favors being done consciously or unconsciously to a specific demographic.
  • Address the unfairness detected by sourcing more training data related to sensitive genders. Apply the new-age de-biasing tactics wherever necessary to limit the errors during NLP for all the genders and demographics.

👉AI in Recruitment and the Possibilities of Gender Bias in HR




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Shanmon Wilson

Shanmon Wilson

HR Director | Certified HR Professional | Successful Business Operator | Talent Identification and Development