AI & Gender

Gender diversity is key to understanding the influence of societal norms for the roles and resources available to men and women. Over the years, several findings have suggested that the preconceived socio-cultural structure has added immensely towards providing unequal opportunity to female counterparts than male counterparts in several areas. With the onset of emerging technologies like AI, one would perceive having an opportunity to reduce the existing gender gap. However, there is a high likelihood that AI can be influenced due to human intervention, and we may see the same socio-cultural dynamics trickling down into the AI transformation. 

Firstly, jobs with softer skills like receptionist, executive assistant, clerical or administrative roles are often reserved for the women, while the decision-making roles are mostly reserved for the men. The same gender stereotype is also observed in the tech firms through virtual voice assistants like Siri, Apple, Sophia, Erica who are subservient female conversation companion bots; whereas the rescue or fireman or coach robots are masculine in shape and form like Hermes and Atlas. Therefore, it is essential to explore the fundamental barriers to equality embedded in the design and purpose of AI technologies.

Secondly, laws and policies surrounding AI are currently at the embryonic stage of development. There has been an abundance of work directing that technological practices should be informed by holding human values at the heart of development. However, there is a gap in understanding how these values can be embedded through policy and legislation. And these structures will play a crucial role in how AI will shape gender dimensions in the future.  Therefore, there is a need for understanding the existing and emerging policies related to AI such as those surrounding privacy and design which will have an impact on gender equality.

Thirdly, another issue that accentuates the discussion on gender in AI is the high level of data under-representation and deprivation  when it comes to representing vulnerable groups. Biased datasets amplify gender and racial inequality and project past and present biases into the future. Therefore gender specific guidelines for identifying best practices in AI may not be possible until guidelines on appropriate data collection and data handling is tackled.

Fourthly, there is significant gender disparity in the AI workforce. Those designing, coding, engineering and programming AI technologies do not exhibit a diverse demographic. Nor does the current pipeline promise a better balance in the future. There is a high likelihood that the technological divide between men and women may rise because lesser women are seen to enter higher studies in technology or coding. Diversification of AI workforce is even more urgent as there will be increased demand for skilled technological experts with growing AI and at the current rate, the existing inequalities would only be aggravated. 

Despite the incumbent challenges, we cannot undermine the fact that AI brings huge potential to bridge the gender gap if implemented in an informed manner. Emerging technologies can create incentives for everyone to outlast and can reduce the gender gap by providing women the ability to gain, improve job opportunities, and obtain expertise; thereby enriching the standard and well-being of individuals and of society as a whole. 


At AI Policy Labs, fostering innovation for accelerated socio-economic development is a priority. And with this vision, AI Policy Labs’ Gender and AI Programme seeks to understand how a gender neutral AI would look like and aim at  

• Facilitating data sets created for learning/developing technologies that are based on diverse inputs from gender, ethnicity, age, sexuality recognising the inseparability of these.

• Advancing a global AI regulatory framework with a strong focus on gender equality

• Making national AI policies/strategies impact the intersection of gender and AI and be made gender inclusive in both process and outcome.

• Exploring the factors that impact diversity in STEM education and in the AI workforce and create a sustainable culture of diversity. To raise the understanding of gender gap, support counter perceptions, tackle current biological differences in learning programs, and create an ecosystem to facilitate more women participation in STEM studies. 

• Creating a dynamically decentralised decision making mechanism integrating the sensitivity and experiences of women in technology development

• Formulating educational programmes to guarantee that people acquire the basic skill sets and technical literacy which are required to engage in the labour and social systems

• Addressing the decline in the proportion of women representation in future technology

This work programme would pursue a multilateral approach facilitating conversations with international stakeholders, technologists, policy experts, technology designers seeking to address the above goals. 


Further, we will build targeted programmes drawing from wider literature, research and ground findings and culminate into capacity building programmes, podcasts, events, research papers, and other medium of communication to advice policy development. These proposals are intended to provoke practical action surrounding the impact of AI on gender equality.