Forum Discussion
Iceberg Ahead: The Unseen Data Divide in AI
85% of AI's training data comes from the global north, highlighting the digitalization gap of the global south. This imbalance is more than just a discrepancy; it represents a profound division that ...
This is indeed a crucial topic, these are some strategies to mitigate it that I have found useful. Curious to read other tactics!
- In the system development phase:
1. Diverse Development Teams: Diversity in development teams is key to incorporate various perspectives and mitigating potential biases.
2. 360-View Mindset: Adopt a holistic approach by involving users, beneficiaries, and other stakeholders throughout the development process to ensure a comprehensive perspective is considered. I can't stress this enough, there were so many times that we were able to correct some of the system's results thanks to the comments from the people who worked close to the beneficiaries.
- In the Testing Phase:
Perform bias analysis: Conduct a thorough analysis of the model's results during the testing phase to identify and address biases. For example, do detailed statistical examinations, including simple descriptive statistics related to gender, age, or other characteristics pertinent to the studied population.
- In the system development phase:
1. Diverse Development Teams: Diversity in development teams is key to incorporate various perspectives and mitigating potential biases.
2. 360-View Mindset: Adopt a holistic approach by involving users, beneficiaries, and other stakeholders throughout the development process to ensure a comprehensive perspective is considered. I can't stress this enough, there were so many times that we were able to correct some of the system's results thanks to the comments from the people who worked close to the beneficiaries.
- In the Testing Phase:
Perform bias analysis: Conduct a thorough analysis of the model's results during the testing phase to identify and address biases. For example, do detailed statistical examinations, including simple descriptive statistics related to gender, age, or other characteristics pertinent to the studied population.