Failing to address this issue risks entrenching discrimination and bias in our systems. Achieving this requires a thorough examination of the foundations of AI development, the data it relies on, and how we identify and tackle bias within its code.during the World Economic Forum .
Women authors contribute to 14% of AI research papers, and women-led firms receive a paltry 2% of venture capital funding. These figures underscore the urgent need for action to address gender bias in AI and promote inclusivity and diversity in the field. When looking at the abovementioned figures, the imminent reality of gender bias is difficult to ignore. The statistics are evidence enough and raises legitimate concerns about the potential reflection of this bias in AI systems.
We must acknowledge the existence of this bias, recognise the significant risk it poses in further entrenching biased practices, and unite in our efforts to actively address and mitigate these biases.
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