The bias conundrum
Bias in machine learning is not a novel concern. It is deeply rooted in the data we collect and the algorithms we design. Bias can arise from historical disparities, societal prejudices, and even the human decisions made during data collection and annotation. Ignoring bias, or addressing it solely through model-centric techniques, can lead to detrimental outcomes.
Consider the following scenarios, which illustrate the multifaceted nature of bias:
- Bias in finance: In the financial sector, machine learning models play a pivotal role in credit scoring, fraud detection, and investment recommendations. However, if historical lending practices favor certain demographic groups over others, these biases can seep into the data used to train models. As a result, marginalized communities may face unfair lending practices, perpetuating socioeconomic inequalities.
- Bias in human resources: The use of AI in human resources has gained momentum for recruitment, employee...
