Genders’ bias in AI is a burning topic of the current times with women from all walks of life condemning the way AI algorithms are getting built and fed. Unconsciously, AI engineers and scientists are feeding biased datasets to smart systems and machines capable of executing on 🔗NLP (Natural Language Processing). AI programs and ML algorithms have been made to learn and identify things in a gender-biased manner which has aroused widespread anger globally.
Alexa and Siri, both have been found at fault when it comes to treating men and women as equals. The examples would include AI machines mostly identifying women as nurses and men as doctors. Another example can be Google voice search for “gorillas” resulting in dark-skin people identified as Gorillas in the image results of the search engine. However, things have been rectified since then. But still, there exists an abundance of sexism when it comes to 🔗AI treating the two genders.
🔗Gender bias is getting executed in AI algorithms based on emotions too. For instance, one gender is thought about being angrier than the other at all times. The long-term effect of emotion AI can prove to be catastrophic as in the future, emotion AI will play a vital role in marketing, at work, and in every kind of process being run across industries.
HR leaders at their respective organizations can dismiss the biases based on gender, but when technology itself makes gender-biases, taking things into control becomes difficult. It becomes even harder to abolish gender bias when 🔗“AI in HR” is already making headlines globally.
In machine learning language, the meaning of bias narrows down to increased errors for specific demographic categories. There exist different variables that researchers must responsibly incorporate in training ML models. Such variables comprise:
👉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.
- Ensuring diversity in the training datasets (utilizing the same number of female audio clips as males in the training input data).
- 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
HR tech has evolved drastically in the last couple of years with the intrusion of disruptive technologies like AI and big data analytics. You type in best hr tech trends on Google, and it will show you hundreds of hr-related articles on the application of 🔗AI in HR.
Talking about the biases based on AI usage in HR, they are limited at the moment, but, if AI continues to treat different genders differently, the future will see HR domain too plagued with AI-based gender bias.
As a civilized society, we have an obligation to be fair and equal to every gender. We hope the pros with AI to outweigh the risks associated. Now, everything lies in the hands of those at the helm of things. In the fight to defeat AI bias, 🔗researchers and AI engineers would need to put in the concerted efforts to balance things out. Fortunately, corrective measures are being taken in this regard and things will get much better in the times to come.
Originally published at https://www.topdigitalblog.com on March 18, 2020.