In September 2017, a research was published by Stanford psychology professor Michal Kosinski and Yilun Wang, in which they claim to have identified the sexual orientation of people by analyzing photos of faces with an artificial intelligence algorithm (deep neural networks). The research has generated heated debates in the US and Europe, and has also been reported in some Brazilian newspapers. But, before explaining how the research was performed, it is important to remember what is the current level of development of artificial intelligence in our era. In a simple explanation, what the various AI computational techniques can do is to identify patterns by analyzing a huge amount of data. That is to say, they are algorithms created to accomplish specific tasks and are still far from resembling an artificial general intelligence, like the human one.
The study analyzed 35,326 images of men and women, both Caucasian, which were collected from public profiles of an American dating website. The researchers justified the use of just one ethnic group in their analysis due to a lack of data representing other ethnic groups, such as Afro-Descendants. In order to classify the sexual orientation of a person, the algorithm analyzes both permanent facial features (e.g. maxillary size, etc) and transient ones (e.g. haircut, makeup, etc.) after been trained with a set of images to learn what were the patterns. In the end, the study claims that their artificial intelligence could accurately distinguish, only with the analysis of one photo, whether a man was gay or heterosexual, in 81% of the cases, and in 74% of the cases for women. The rate of success for humans analyzing the photos was 61% for photos of men, and 54% for those of women.
Following the publication, two major LGBTQ rights groups, the Human Rights Campaign (HRC) and the GLAAD (Gay & Lesbian Alliance Against Against Defamation) in the United States have heavily criticized the research. The GLAAD’s Chief Digital Office, Jim Halloran, said that:
“This research isn’t science or news, but it’s a description of beauty standards on dating sites that ignores huge segments of the LGBTQ community, including people of color, transgender people, older individuals, and other LGBTQ people who don’t want to post photos on dating sites.”
And HRC Director of Public Education and Research Ashland Johnson stated that:
“This is dangerously bad information that will likely be taken out of context, is based on flawed assumptions, and threatens the safety and privacy of LGBTQ and non-LGBTQ people alike. Imagine for a moment the potential consequences if this flawed research were used to support a brutal regime’s efforts to identify and/or persecute people they believed to be gay.“
In addition to the ethical and methodological issues about the research itself, it still touches on the debate about what should be the limits for classifying social groups through automated decisions, based on the analysis of large amounts of data, whether for research, commerce, national security, policy-making, among others. Similar criticisms may apply to various online services which we use on a daily basis for “free” in exchange for our personal data. While an academic research can be publicly criticized and debated, the use of Artificial Intelligence and Big Data by governments and companies present additional risks related to guarantees of transparency.
To help raise public awareness is interesting to mention a metaphor used by the scientific writer Franklin Foer, comparing the digital companies with the food industry. In the second half of the 20th century, especially in the US, the market for processed foods and ready-to-eat meals took off, presenting a future in which people would not waste time preparing meals. It was only after decades of debate that people have perceived the price to be paid for this convenience, such as the harms derived from the consumption of large amounts of sodium and sugar; and, also, its environmental impacts, due to the production chain of these industrialized foods. Therefore, similar to the food industry, maybe only now we are freeing ourselves from an era of dazzle with digital companies.
To help people who are not from technical areas involved with Artificial Intelligence and Big Data, it is useful to indicate Carl Bergstrom and Jevin West`s reasoning. They recognize that many people may be reluctant, at first, to criticize the use of a statistical or an AI technique, because they do not understand its functioning. However, most of the problems, such as social and ethical ones, are related to either (1) the initial data (input), which will be used by the algorithm as in a problem of biased data; or (2) the problem lies in how the final results (output) were interpreted. Thus, the focus of the analysis should be, in other words, what goes in and out of the algorithm.
While developed countries present a large amount of public concern, it is necessary to foment greater discussions in Brazil on the use of these technologies and what will be their impact on the protection of human rights. As new industries begin to incorporate these technologies, new ethical and social problems will arise, thus, it will be necessary to include on the debate the various social segments affected by Artificial Intelligence.