If you can't measure it, it doesn’t exist

Social responsibility


19 March 2019

Data is a key element in the technological revolution that will help us tell the story of women.  So, this month, because the role of women is in the spotlight, it's a good time to reflect on this.

Let's start with some figures. At the time this article was written, there were about 3,807,418,192 women in the world, and in Europe they accounted for 51% of the total population. However, according to The Economist's Glass Ceiling Index report, with data from Spain, women are clearly underrepresented in the labor force. 

Eurostat has also published a report about the lives of men and women in Europe. This study reflects women's daily lives: we emancipate two years earlier, 3% more women than men go to college, we enter the workforce one year later than men, the unemployment rate is 11% higher for women, and they have a higher life expectancy by five years.

However, these statistics reflect an incomplete story, because to draw a true portrait of reality and build upon the information available, there is a huge data gap in relation to women. This was denounced this year by the Bill and Melinda Gates Foundation in its annual letter, in which it is clear that there is a lack of data available to be able to describe the reality of about half the population. This report emphasizes that there is an artificial separation between the problems of men and women, that the available data is biased or unrepresentative and, despite current technological capabilities and the 2.5 billion gigabytes generated every day, basic questions cannot be answered about women, especially in less-advantaged countries.

Quality data is therefore urgently needed. What does this mean? The Data2X organization explains the importance of the qualitative sphere in this article: "Good data about women and girls must be, above all, of high quality; that is, it must be reliable, valid and representative, and free from gender bias. Good evidence must also have good coverage, including coverage for Spain and regular production in the country, and must be comparable in all countries in terms of concepts, definitions, and measures."

This "good data" is the first step in making effective decisions regarding public policies or new services and ensuring that they reflect a complete view of reality, and of society in its diversity. From this perspective, it really is possible to address the decisions about solving problems such as poverty, unemployment, and inequality, which have a substantial impact on the advancement of society.

On a positive note, this lack of analysis is now known, and has led the Bill and Melinda Gates Foundation and other organizations such as Data2X and the United Nations, to launch data collection initiatives broken down by gender.

The experience of information gatherers reveals an additional positive aspect from these data collection expeditions. When African women are asked about their daily activity, not only is valuable information being collected, they also understand that their reality matters.

The desire to know about, and the need to study the lives of women implies that there is a willingness to measure what happens to this half of the population. And this directly reflects that we, as a society, value women when looking to the future, because what isn’t measured doesn’t exist.

Data: The Key to the Future

Having this information is of paramount importance for raising awareness of and analyzing the situation of women in societies and is a substantial tool for designing the future. Why? Because data is the element that feeds the technological disruption that is transforming society: Artificial Intelligence (AI). So, if there is insufficient data on more than half of the population, how will Artificial Intelligence learn about it? How can we guarantee that there will be no bias in the decisions made?

Let's consider an example. Amazon has launched a system with Artificial Intelligence for hiring talent. The results revealed that the system fell into the systematic bias of discarding women's resumés. The explanation lies in the fact that Artificial Intelligence learned from a greater number of male profiles, and how they progressed in the company. Reality indicates that the human biases that already exist are perpetuated. The solution is to teach the machines of the future about the society we want, just as we do for boys and girls.

Soon, Artificial Intelligence will be able to perform any cognitive task at least as accurately as humans. We are now witnessing the take-off of this technology and therefore, when it becomes established, I want to imagine an Artificial Intelligence of the future that thinks like a woman and programs like a girl. This is only possible with good data.



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