Girls in Tech Nepal recently organized a panel discussion titled ‘Need of Women in Data against Gender Bias in AI’ on the 20th of March 2022. The event was seen as a remarkable move for Girls in Tech Nepal on its mission to put an end to the existing gender biases in the tech industry. It was supported by Development Initiatives, The Asia Foundation, UKAid Nepal, and Data for Development Nepal in presence of renowned data scientists and top-level executives as the panelists with around 65+ audiences representing various organizations.
Similarly, Aanchal Kunwar, Managing Director of Daraz Nepal, Sachin Karanjit, Director of Nepal Operations – Deerhold, Shreyasha Paudel, Research Fellow at NAAMI, and Rakesh Katuwal, Manager of AI Services and Product Department at Fusemachines were the panelists.
The event covered how the biases in data can lead to algorithmic biases including the use of algorithmically biased technology and its significance in policy-level decision-making that impacts every facet of life.
Amidst the event, Ms. Aanchal Kunwar stated, “At Daraz Nepal, we do mystery buys – random buys by Daraz employees as a customer to ensure that we do not only use majority-driven data to make decisions.”
Similarly, Shreyasha Paudel, Research Fellow at NAAMI said, “The biases will always exist. It is up to us as engineers, executives, leaders, and policymakers that we inculcate our values to mitigate those biases.”
Mr. Rakesh Katuwal shared his thoughts too stating, “Weighing data privacy against data accessibility and creating roadmaps for utilizing the data. Stringent policies to protect our information, are they in place?”
Likewise, Sachin Karanjit, Head of Nepal Operations at Deerhold commented, “It is the responsibility of executives and businesses leaders to set examples regarding gender biases.”
And the discussion concluded by presenting the significance of prioritizing social change in addition to different solutions including:
- Building platform identifying biases
- Auditing existing software which are algorithmically biased
- Creating inclusive and diverse training sets
- Thinking of the social impact of technology that we’re developing
