Digital series 2: Bias in Artificial Intelligence (“AI”)

Date: 19/05/2021
Time: 6:00 pm - 7:00 pm
Cost: Free

Location:

Virtual event


Details

Purpose
To unpack the challenges faced by AI, or 4IR regarding the biasness in the algorithm. The biasness include but not limited to Gender; Race, Sexuality, Ethnicity. The biasness can either be Implicit or Explicit.

Discussions
The WEF 2021 Global Gender Gap Report revealed that it will take more than 200 years to close the gender gap.

With regard to Future of Work; it revealed that Gender gaps are more likely in sectors that require disruptive technical skills. For example, in Cloud Computing, women make up 14% of the workforce; in Engineering, 20%; and in Data and AI, 32%. While the eight job clusters typically experience a high influx of new talent, at current rates those inflows do not re-balance occupational segregation, and transitioning to fields where women are currently underrepresented appears to remain difficult. For example, the current share of women in Cloud Computing is 14.2% and that figure has only improved by 0.2 percentage points, while the share of women in Data and AI roles is 32.4% and that figure has seen a mild decline of 0.1 percentage points since February 2018.

This is alarming as it demonstrates that even in this innovative, technological sector where new hires are high, there is still a gender gap with regard to representation.

Hence, the codes written for AI represent data points that are historically biased as they never represented the diversity of gender.

The panel of speakers will unpack the reasons behind bias in AI, in particular, Gender bias; how can this bias be resolved? What are the challenges? Does it need regulators or policymakers to deal with it? As leaders in organisations- how do you determine when AI is used to ensure that there is no bias in its determination – Quality?

Outcome
The outcome of the discussion will be to educate members on the challenges faced by AI and come out with an advocacy strategy of identifying how to ensure AI is not biased.

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