CEO and co-founder of Shaip, Vatsal Ghiya has 20 years of experience in healthcare AI software and services and enabling on-demand scaling of business processes with machine learning and AI initiatives. This guest feature, Vatsal Ghiya has shared key insights on how to eliminate bias in Conversational AI.
The Key Takeaway from the Article is-
- As per statics reveal, the accuracy rate of fetching results through voice search on American males is 92%but this goes down to 79% and 69% for white American females and mixed American females. This is one classic example of Bias AI.
- Some real-world examples of the bias AI include Amazon and Facebook where males were favored more during recruitment in Amazon and Facebook target the customer as per their gender, color, and religion. This bias in AI is caused by three reasons and these are data, people, and technology.
- To eliminate the bias of AI from any application and system, organizations can follow the measures like- certifying the data sources and quality, monitoring the model in real-time and analyzing the diversity of data before using AI in their operations.
Read the full Article here:
