Digitalisation Dismantling or Reinforcing Gender Based Inequalities Poster.jpeg

Digitalisation: Dismantling or Reinforcing Gender Based Inequalities Poster. This poster attempts to highlight the immense potential of technology to dismantle gender-based inequalities; while pragmatically addressing the counteractive challenges it currently poses, by possibly reinforcing such inequalities. The Sustainable Development Goal (SDG) 5 pledges to achieve gender equality and empower women, and Target 5b of this SDG pledges to “enhance the use of information and communication technology to promote the empowerment of women” Over the last 25 years, the digital revolution, marked by the shift from analogue to digital technologies, has been characterised by technological advances ranging from smart phones, the mobile Internet, the Internet of Things (IoT), Artificial Intelligence (AI), machine learning, (big) data, social media, cloud computing, robotics, and much more. These technologies have touched every domain, including health care, commerce, education, manufacturing and finance. Digital technologies could significantly improve female participation in economic activities and enhance their social autonomy. Certain technologies offer women the potential to bypass some of the traditional cultural and mobility barriers that they face offline. For example, in the “Chance” section of pur poster, we have highlighted an instance of emerging AI based bots, named as the #MeToo Bots which can identify and flag potential sexual harassment, bullying or blackmail over digital communication platforms. However, in the “Challenges” section, we point towards the continued female gendering of virtual personal assistants (VPAs), such as Alexa by Amazon, Siri by Apple and Cortana by Microsoft. This stereotype has seeped into Indian companies like IRCTC too, which introduced DISHA, a virtual assistant to assist online train ticket booking. These instances highlight the ramifications of biassed designs of AI models. While developers justify this by citing ‘likeability’; the traditional stereotypes about the role of women as obedient, subservient and ‘domesticated’ are further amplified by such gendering of VPAs. On a heavier note, in the “Commination” section we see that the AI models trained using datasets generated in an unequal society; tend to amplify existing gender-inequities, turning human prejudices into seemingly objective facts churned out by biassed algorithms. A ‘feedback loop’ thus generated is constantly shaping the AI industry and its tools, creating a gendered vision of the world which is embedded into AI technologies. As we know, AI models follow the GIGO or Garbage In Garbage Out tenet; that is, they generate data based on the data fed to it. Examples of gender-biassed recommendations made by machine learning and AI models have emerged across many different algorithms and applications, from: Word embeddings trained on Google News articles that label computer programmers as male and home-makers as female to Apple Card assigning a woman a lower credit limit than her husband who possessed a worse credit score; and more rec ChatGPT providing gender-biased answers, assigning the roles and duties of a “homemaker” to, and I quote “typically a woman”. Another example of technology reinforcing gender inequalities is the Absher App in Saudi Arabia which has been abused by men to track and control their women dependent’s movements, reinforcing the country’s system of male guardianship. On the brighter side, biometrics linked digital identification have made it easier to ensure that social welfare schemes and subsidies are provided to deserving women beneficiaries, instead of being availed by male family members. The World Wide Web has also helped enhance the social autonomy of women. Certain technologies offer women the potentiabypass some of the traditional cultural and mobility barriers they may face offline. For example, women unable to join the Mahsa Amini protests in Iraq and also women during the #MeToo movement, particularly those who were typically constrained by deeply rooted patriarchal structures, recorded and shared their support on social media platforms such as Facebook and Twitter. Cell phones with internet facilities and affordable internet plans are also helping domestic workers utilise on-demand apps like: MaidHub, UrbanClap, Bai-on-Call, etc. to find work. These apps allow women-workers to be fairly remunerated, as they provide a record of the exact duration of their work and the exact amount to be remunerated, all because these apps carefully track the information of every work assignment they receive. On the other hand however, gender digital literacy gaps mean that male family members may engage with the platform on behalf of women – and therefore control their income and work lives. On a separate note, in IT and STEM domains there is a significant gap in terms of education, training and job opportunities for women. The structural inequality of opportunities available for women in the workplace severely limit their participation in the design and development of new digital technologies, and form the part of a feedback loop which further reproduces biases against women. There is an inherent stereotype that technology is for men only, which is apparent from colloquial terms like “brogrammer”. Even when opportunities in IT firms are presented to women, they are often assigned nominal tasks of creating slide desks, framing emails, putting together corporate get togethers, etc. instead of being assigned technical responsibilities within teams. The prevalence of such ‘masculine defaults’ in tech workplaces result in micro-aggressions, subconscious biases, sexual harassment and other forms of discrimination such as demeaning comments against women. These stereotypes are also evident from statistics in the technology startup ecosystem. As of 2021 only 1.9% of tech startups in a developed country like the US had women founders. Similarly, as per a Forbes 2018 report, 93% of VC funds raised in Europe went to all male founding teams. But things seem to be improving; agencies like the Global Fund for Women’s Technology Initiative are working towards not only ‘closing the gender gap’ in access to control and shaping of technology; but also empowering women through STEM and IT education investments. Mobile phones and digital platforms are already benefiting female entrepreneurs by connecting them to markets, providing multilingual training, and facilitating their collective action. For example, in India, the Self Employed Women’s Association (SEWA) supports networking for women entrepreneurs and provide them access to market information on their mobile phones. To conclude, I would like to quote the 20th century Canadian philosopher, Marshall McLuhan: “We become what we behold. We shape our tools, and thereafter our tools shape us.” and I say, the same applies for technology as well, especially when it comes to dismantling gender-based inequalities.

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