CIO OPINION
Just as academia , industry and governments came together to find solutions during the height of the pandemic , we can take the same approach to reduce AI bias .
A recent World Economic Forum article highlighted three focus areas including more foreseeable design processes that give greater consideration to predicting the impact of AI systems and more emphasis on inclusivity across gender , race and class .
User testing involving representatives from diverse groups is another recommendation to garner a wider range of views and insights before launching AI solutions .
STEEPV analysis was also put forward as a way to enhance fairness and non-discrimination .
Based on these developments , pre-and post-AI era data will probably be rated differently in the future .
Where and how do the biases originate ? They are frequently traced to biased datasets or datasets that under-represent or ignore whole populations . These biased sample sets – which are used to train AI models – produce untrustworthy outcomes .
How to eliminate AI bias
Eliminating bias is widely discussed today but it is a big challenge because bias comes from human and tech-driven data bias . Although human bias is almost impossible to remove , we can create fairer , more ethical and transparent data-gathering processes to train AI models .
This is an analysis of external environments covering social and political attitudes , demographics , cultural priorities and the tech and economic landscape to build a bigger , clearer picture .
We have steps to move forward , and just as academia , industry and governments came together to find solutions during the height of the pandemic , we can take the same approach to reduce AI bias .
The need to act is now and we can find a way to make AI more trusted and adopted .
From my discussions with stakeholders in Singapore , there is widespread consensus over this and taking on AI bias sooner , rather than later , will truly serve everyone ’ s best interests . p
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