A few weeks ago I was listening to a podcast which invited Luis Ballesteros, an Assistant Professor at The George Washington University to speak to research on a book that he contributed to, authored by Howard Kunreuther and Michael Useem entitled Mastering Catastrophic Risk: How Companies are Coping with Disruption. The professor echoed the intelligence of the role that not only government, but most audibly, the private sector in aiding the relief of pandemics in their respective economies such as the novel coronavirus 2019 (COVID-19). Another point of conversation that was lightly touched on was what the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) in their “Global Humanitarian Overview 2020” report , estimated to be 168 million, the most vulnerable people in the world, prone to the recent pandemic and what could be done for them. This is what we’ll unpack in the next few paragraphs, but first, let’s lay a foundation for what Covid-19 is before we dive into how we as society, together with other respective stakeholders can enable preparedness from a gendered impact perspective economically and socio-economically.
First detected in China in December 2019, COVID-19 has since spread to 169 regions or countries and more than 329 000 cases globally, with Italy leading with 5 476 deaths as on 23 March 2020 according to the John Hopkins University and Medicine Coronavirus Resource Centre. The numbers of the cases are expected to rise exponentially over the next coming weeks and months, and cases in developing nations too are rising, with the vulnerable susceptible to the exposure in different parts of the world.
For this particular article, we’ll focus on the gendered implications of COVID-19 in affecting the vulnerable, exploring the measurable impact that this pandemic will have on women, and how past data proves that women and young girls are going to become the most affected economically and socio-economically, and present ideas on how to futureproof this risk for the public sector, private sector and civilians.
One of the consequences of poverty is gendered discrimination, which means that women are disproportionally burdened when the assault of catastrophic events such as COVID-19 take place. In the past, episodes like the 2010 Haitian cholera outbreak, the 2014–16 West Africa Ebola virus disease (EVD) event and the 2016 Zika boutade, with research supported by the Interagency Standing Committee (IASC) shows how this burden of caregiving is entrusted with the consequences of the risk of infection, being responsible of running of the household and the prevention and rescue tactics with being exposed to mental and physical harm and the economic role of sustenance provision by seeking financial assistance. With COVID-19, although men (and older people) are much more prone to mortality rates and being infected, it’s the caregivers who are engaging with the risk, and women compose of larger parts of the health workforce
In realising the data and intelligence of both the past and present, here are some measures of what can be done in creating six (6) inclusive response measures:
· Misinformation spreads fear faster than COVID-19 itself and leads to practices that resist the acceleration of the healing of the exposed and infected. It’s important to be proactive in sharing information, as it is in creating responses that are intersectional in how they’re being consumed and analysed. Languages, gender, nationality, disabilities, economic status are important utilities in creating the information and sharing it to ensure no discrimination. Instilling behavioural change should also mean to allow for this information to be accessible and affordable for civilians by zero-rating certain websites and using traditional media which the greater population has access to and can afford.
· The rise of gender-based violence and sexual exploitation cases in times of outbreaks affects women and other marginalised groups, and investing in organisations that are already on the ground and with access to mobile services that can reach urban, peri-urban and rural areas where the women will be affected is important.
· A notable effort from the public and private sectors is the relief financing for not only employees, but for small business owners who will be heavily impacted by COVID-19, and these include holidays and funds set up by economic development ministries globally like the Federal Coronavirus Small Business Assistance in the US and the Debt Relief Fund in South Africa.
· COVID-19 calls upon social distancing, or as the World Health Organisation (WHO) encourages, “physical distancing”, and this is as a phenom that is supporting of trying to limit the spread of the virus as it is classist. In developing markets, the informal sector makes a large contribution to the GDP of a nation, as well as the majority of the (low) income earners of the population and the trading is offline. What happens when physical distancing discriminates against those unable to do so as it affects their means of creating income? Provision for a fund towards the lower-income earning women and informal traders that pays out the average income earned or minimum wage (of a nation) and trainings to upskill.
· The previous proposal above links much to this next one: (greater) Investment in research and development that will inform gendered solutions during catastrophic events. According to “A Gendered Human Rights Analysis Of Ebola And Zika: Locating Gender In Global Health Emergencies” study, less than 1% of published research papers on previous health pandemics were on the gendered implications and dynamics of such outbreaks. We cannot be prepared for an emergency like COVID-19 and deploy resources without the informed resources of how and who to deploy the unique resources to.
· In order to create and implement policies that are focused on addressing a population that will be impacted the most, representation matters. Now is the time to continuously raise the profile of women in global health and ensure thought leadership is not only bound to the stages of conferences, but authoring research papers and sitting in boardrooms influencing policies that are nuanced to the unique solutions needed.
There’s no cure for COVID-19 at present, and countries like China and the US are racing to find a vaccine to ensure that the accelerated effort of the virus to kill and infect minimizes. In the meantime, as global citizens, it is out duty and responsibility to keep each other accountable for physical distancing, being self-quarantined or isolated, people’s lives depend on it. Out of this pandemic, is the hope for more research into the gendered implications of such events and the opportunities for economies, the greater society and the private sector to invest in inclusive measures that are focused on enriching the economic prowess of women, and their participation in global health.
There is no better time in developing markets and the current industrial revolution than right now to contribute to the discussion of the Fourth Industrial Revolution (4IR) and the alarming PR messaging that it has, some well-intentioned misdirection and the other half split with an overflow of information of which skillset to prioritise and which technology to employ. The job losses, the new technology and the illiteracy to name a few of this incoming era can indeed create a barrier of intimidation on entry, and what adds to the complexity of the situation is that the data lies.
Exploring Data Bias
You should be familiar with the notion of data being the new currency, at least in comparison to oil as an infinite resource that can empower economies. And data, having been undocumented, raw and undigitized has always been around, it is rather the scramble for the science and technology of it, and who gets access to it first that impacts the narrative and gets an opportunity to score some points for their industry, economy or group of privilege that they belong to. It’s the data scramble, it’s the data rush. This is what’s caused, I believe, the insurmountable backlash and inaccuracies, the product bias towards chatbots or products otherwise, whether its towards gender, race or access. The question that then follows up to this statement would be where the data is, and exploring the intentional bias and opportunities for solutions to the bias, and what stakeholders can do create inclusive economies.
The Impact of Bias Practices …
Machine Learning which is an application of Artificial Intelligence (AI) that studies the sciences of how machines can automatically learn and improve from experience by learning from themselves, is learning from the bias of the producers of the algorithms, and these makers of algorithms are largely white males as can be seen in an example of this through facial recognition products created by IBM, Microsoft and Face Plus Plus. That means that, so is the (informed) data, which breeds much room for prejudice.
A recent example of a sector that informed this bias is financial services, mostly with credit, and is now building the intelligence tools to either enforce or break away from this. In South Africa, usury expert Emerald van Zyl, claims that Standard Bank (including banks like First National Bank), which is Africa’s oldest bank is currently under hot fire for billing its black customers at a higher interest rate in financing. This is not the first time this occurred with Standard Bank, as in 2012 they were also charged with violating the National Credit Act where eventually customers were refunded by 2013. Now, if the machine learns these algorithms and continues to grant the same product bias, the discriminatory practices are more than likely continue.
This is kind of problem is also consistent in the health sector. In a New England Journal of Medicine article published on 15 March 2019, researchers of the Framington Heart Study showed the risk and capability of AI algorithms to demonstrate bias. The research used AI to predict the risk of cardiovascular occurrences in non-white populations and the results demonstrated bias in both over- and underestimations of risk.
People's lives are at stake through the products of 4IR. And, beyond the glitter of Sophia The Robot and the New Generation Kiosks at companies like McDonalds, there is a community that is not being intentional about being inclusive and rather duplicating structural socio inequalities that implicates another.
Data bias does only one thing, it mirrors what is socially ingrained, which means that it lies and tells a partial truth, of which is not meant for consumption by those who produce it.
Dismantling the Structural Bias
The call for inclusive economies goes beyond teaching young, black girls how to code and having strictly women only data science clubs. Practices like hiring more diverse teams leads to impactful and informed product creation and is a good contribution to mitigate prejudice algorithms and encourage more accurate data on a model. A sub-division of AI, Natural Language Programming (NLP) is a study that is concerned with the processing of computers and human natural languages, and can be used as a great example and opportunity for the necessity of the inclusive call in the sector. Translating open source of data sets in different parts of the world requires an understanding of the language being translated so that we can not only have Siri being able to understand my instructions in English but also the opportunity to preserve and digitise languages like the Khoi which are diminishing, mostly because, especially with African languages, the impartation of language happens orally. A great example of this opportunity is Ajala Studios, which a Nigerian startup that builds natural language and speech processing applications for African languages, which means that they can too synthesize speech from African languages presented as digitized text, a gap that’s mostly recognises Western accents, voices and names.
The responsibility of creating these opportunities is also a shared responsibility, especially with the public sector. Governments in both developed and developing markets need to invest more in Research and Development (R&D) and in the social concept of open innovation (engaging the public with the data) especially as the impact of this investment is quite telling. And although it is a long term investment, the return on this investment is worthwhile. Researchers from the United Kingdom (UK) and Saudi Arabia looked at 40 Asian counties and how their spend on R&D lead to the production of quality research publications across sciences and social sciences; and with more research in the UK showing the positive impact that public investment has in the increment of private sector investment and in attracting foreign direct investment. Through this R&D investment and its impact in the knowledge economy, it also presents an opportunity to lead to more computational intelligence and feeding it the missing data, and the greater economic impact through the indicator of Gross Domestic Product (GDP).
The next solution is not only costly but risky, but if there is one thing that I’ve learnt about being in the innovation space, whether the product is out to market or still in the proof of concept phase, no matter how good it looks on paper, it’s that it is never too late to take the product off market if it doesn’t serve its purpose. A great example of this is Vodacom South Africa’s failure, thrice to launch its sister Kenyan network SafariCom’s M-Pesa to the South African market. Factors like an onerous regulatory environment, the competitive advantage that the larger and established banks have with their products to low-income consumers, and some have also argued due to the mixed messaging upon launching, from introducing it as a mobile money wallet to a platform that is linked to your VISA card. This case study is also an example of the danger of wanting to copy and paste a one-size fits all product into an Africa that is not a country.
At the end of the day, it's about investing in the visibility of the communities so as to include better, impactful and innovative products and profitability for all ecosystem stakeholders a part of the operation chain.
The data samples ARE there. And unfortunately (or an opportunity), so is the bias. But all is not lost, not with the desire of visionary stakeholders to operate in a transformative world that uses the enabler of technology for sustainable good business.