Tag: ai

A new study in Denmark used machine learning techniques on photographs of faces of Danish politicians to predict whether their political ideology is left- or right-wing. The accuracy of predictions was 61%. Faces of right-wing politicians were more likely to have happy and less likely to have neutral facial expressions. Women with attractive faces were more likely to be right-wing, while women whose faces showed contempt were more likely to be left-wing. The study was published in Scientific Reports.

The human face is highly expressive. It uses a complex network of muscles for various functions such as facial expressions, speaking, chewing, and eye movements. There are more than 40 individual muscles in the face, making it the region with the highest concentration of muscles. These muscles allow us to convey a wide range of emotions and perform intricate movements that are essential for communication and daily activities.

Humans infer a wide variety of information about other people based on their faces. These includes judgements about personality, intelligence, political ideology, sexual orientation and many other psychological and social characteristics. However, while humans make these inferences almost automatically in their daily lives, it remains contentious which exactly characteristics of faces are used to make these inferences and how.

Study author Stig Hebbelstrup and his colleagues wanted to explore whether it is possible to use computational neural networks to predict political ideology from a single facial photograph. Computational neural networks are a class of algorithms inspired by the structure and function of biological brains. They consist of interconnected nodes, called artificial neurons or units, organized into layers. Each neuron takes input from the previous layer, applies a function, and passes the output to the next layer.

The primary purpose of computational neural networks is to learn patterns and relationships within data by adjusting the connections between neurons. This learning process, often referred to as training or optimization, is typically achieved using a technique called backpropagation. This means that after an error is made in the outcome, changes are applied to the functions in preceding nodes in order to correct it.

To train this neural network, researchers used a set of publicly available photos of political candidates from the 2017 Danish Municipal elections. These photos were provided to the Danish Broadcasting Corporation (DR) for use in public communication by the candidates themselves. The authors note that these elections took place in a non-polarized setting. The candidates have not been highly selected through competitive elections within their parties and are thus referred to as the “last amateurs in politics” by Danish political scientists.

The initial dataset consisted of 5,230 facial photographs. However, the researchers excluded photos of candidates representing parties with less-defined ideologies, that could not be classified as left- or right-wing, photos of faces that were inadequate for machine processing, and those that were not in color.

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The new artificial intelligence (AI) tools that are quickly replacing traditional search engines are raising concerns about potential political biases in query responses.

David Rozado, an AI researcher at New Zeland’s Otago Polytechnic and the U.S.-based Heterodox Academy, recently analyzed 24 leading language models, including OpenAI’s GPT-3.5, GPT-4 and Google’s Gemini. 

Using 11 different political tests, he found the AI models consistently lean to the left. In the words of Rozado, the “homogeneity of test results across LLMs developed by a wide variety of organizations is noteworthy.” 

LLMs, or large language models, are artificial intelligence programs that use machine learning to generate and language.

The transition from traditional search engines to AI systems is not merely a minor adjustment; it represents a major shift in how we access and process information, Rozado also argues.

“Traditionally, people have relied on search engines or platforms like Wikipedia for quick and reliable access to a mix of factual and biased information,” he says. “However, as LLMs become more advanced and accessible, they are starting to partially displace these conventional sources.”

He also argues the shift in the sourcing of information has “profound societal implications, as LLMs can shape public opinion, influence voting behaviors, and impact the overall discourse in society,” with the U.S. presidential election between the GOP’s Donald Trump and the Democrats’ Kamala Harris now just over two months away and expected to be close. 

It’s not difficult to envision a future in LLMs are so integrated into daily life that they’re practically invisible. After all, LLMs are already writing college essays, generating recommendations, and answering important questions. 

Unlike the search engines of today, which are more like digital libraries with endless rows of books, LLMs are more like personalized guides, subtly curating our information diet. 

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