We already trust more in machines than in humans themselves

Analyst and researcher, Gemma Galdón founded Eticas Consulting in 2012, a company that performs algorithm audits so that companies are aware of the biases that these data with which they work may have. In this way, the aim is to avoid situations of unequal opportunities or discrimination, thereby improving transparency.

– How and when did the idea of forming Eticas Consulting arise?

– When I finished my thesis, almost ten years ago, they continued to offer me a lot of funding to research. It was not often that there were people working halfway between the social sciences and technology, nor that someone so young, without a fixed place at the university, received this financial support. When I spent a million euros in external financing, they invited me to leave. I took the projects home with me and had to set up a legal entity to carry them out. Then I finished them and hired people. But we continued to raise funding to research issues of technology impact on people. After a few years, I wanted to be more proactive and respond to this research demand to better understand how technology impacts society.

– What difficulties did you encounter?

 

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– I always say that we have been very lucky. Without wanting to, we have the interest of a lot of public and private actors. I never raised something like that. Everything that is supposed to have an economic organization has overwhelmed me and it has cost me a lot, especially to find support spaces that understand what it is to be an entrepreneurial woman. If you are not born with that entrepreneurial spirit, you are very lonely. Sometimes he did not ask for money, but only for help.

– Why did you start then?

– For responsibility. Your projects are like your children. I did not have an entrepreneurial spirit but I am committed to completing this investigation. And there have been difficult challenges during these ten years. We no longer have life and death projects, although I think algorithmic auditing should be standard. There are people affected by bad algorithmic decisions, but there is no visibility of this. That is why what we do is gratifying; we are useful.

– When do you notice that there is a problem with the algorithms?

– By socio-technical characteristics. There are many people who use technology from the outside, I use it from within. He was aware of the problems there were, but also that if you change the way you code, you eliminate that social impact. These problems are not made in bad faith, but out of ignorance. They had not explained to the engineers that, if they did not take into account each specific group, those groups would be discriminated against.

– What are those obstacles?

– There is a lot of talk about biases, because they are permanent. But algorithms are made to bias and discriminate. The issue is how and to whom. Algorithms always reward the norm, which is more abundant. For example, banking systems are based on historical series, so they continue to reward men. The ‘big data’ of the past is taken and it is concluded that the best client is a man, while the woman is a risk client. They are given ten times more credit, even though the woman’s record is better and we are better payers. Nobody cleans the data to avoid that statistical discrimination.

– How can these biases be fixed? Is it just technology?

– Tech is the problem and the solution at the same time. It would be great if there was more digital literacy and the engineers were more humanistic. Artificial intelligence and data have remained very closed in engineering, but they must have notions of social impact. For that you have to make multidisciplinary teams. I have been seeing this for ten years and there is a breach of trust between society and technology. It has abused citizens a lot and now we must recover our space, make a better technology.

– Is the population aware?

– No, because there is a lot of opacity regarding the processes. We should know when we are subject to automated processes. But I think it will change.

– And the companies?

– Either. There is no bad faith, but ignorance. Technology is believed to be magic. We assume that a person can fail, but not that technology can. We already trust machines more than humans and we don’t protect ourselves from bad algorithmic decisions. And some are of poor quality, leading to poor programming decisions.