Diversity and inclusion is a CX opportunity but change must start at the top | Marketing Magazine
When solving the issue of bias, think like a data scientist
The impact of bias in machine learning (ML) and artificial intelligence (AI) systems has become increasingly topical. Greater demand for the commercial advantage gained from AI has caught the eye of business leaders, product managers and marketers alike.
In ML, training datasets will often contain inherent prejudicial biases that reflect the history of that system. However, if not consciously managed, unintentional algorithms based on data containing such biases will result in outcomes that only serve to propagate these biases, unfairly discriminating against certain groups or population segments.
The future will continue to look like the past if we do not actively and consciously attenuate against these inherent biases. Algorithmic solutions to managing bias in AI continue to be an active area of research. However, to mitigate these impacts, we must first accept they exist, actively identify them and have the courage of leadership in strategic decision-making to change future outcomes.
Google’s Responsible AI Practices highlights the concept of fairness, stating that “fostering an inclusive workforce that embodies critical and diverse knowledge” is crucial to combat bias. Effectively addressing bias in your workforce will translate to more inclusive customer management strategies being developed by your leaders.
A diverse, inclusive and multi-disciplinary team will produce equally diverse, inclusive and robust outcomes and lead to spontaneous innovation.
“When solving the issue of bias, think like a data scientist”