A thing of beauty is a joy forever: its loveliness increases; it will never pass into nothingness.John Keats
A recent paper published by researchers at the University of Helsinki and University of Copenhagen investigated whether a computer would be able to identify the facial features we consider attractive and, based on this, create new images matching our criteria. The researchers used artificial intelligence to interpret brain signals and combined the resulting brain-computer interface with a generative model of artificial faces. This enabled the computer to create facial images that appealed to individual preferences.
The artificial intelligence platform was trained in advance with over 200,000 faces of celebrities. So, there was already an inherent bias codified into the training.
The test subjects where connected with multiple sensors to their scalps to measure brain responses to the visual stimulation caused by the subject viewing this library of faces. The responses where logged and the preferences were classified. Eventually, a perfect profile evolved from the many desirable faces. The AI platform then generated new faces that contained the composition of the desired faces to derive a new fictional face that meant the subjects opinion of a beautiful face.
Initially, the researchers gave a generative adversarial neural network (GAN) the task of creating hundreds of artificial portraits. The images were shown, one at a time, to 30 volunteers who were asked to pay attention to faces they found attractive while their brain responses were recorded via electroencephalography (EEG).
“It worked a bit like the dating app Tinder: the participants ‘swiped right’ when coming across an attractive face. Here, however, they did not have to do anything but look at the images. We measured their immediate brain response to the images,” says Senior Researcher and Docent Micheil Spapé from the Department of Psychology and Logopedics, University of Helsinki.explains.
The researchers analyzed the EEG data with machine learning techniques, connecting individual EEG data through a brain-computer interface to a generative neural network.
“A brain-computer interface such as this is able to interpret users’ opinions on the attractiveness of a range of images. By interpreting their views, the AI model interpreting brain responses and the generative neural network modelling the face images can together produce an entirely new face image by combining what a particular person finds attractive,” says Academy Research Fellow and Associate Professor Tuukka Ruotsalo, who heads the project.
To test the validity of their modelling, the researchers generated new portraits for each participant, predicting they would find them personally attractive. Testing them in a double-blind procedure against matched controls, they found that the new images matched the preferences of the subjects with an accuracy of over 80%.
“The study demonstrates that we are capable of generating images that match personal preference by connecting an artificial neural network to brain responses. Succeeding in assessing attractiveness is especially significant, as this is such a poignant, psychological property of the stimuli. Computer vision has thus far been very successful at categorizing images based on objective patterns. By bringing in brain responses to the mix, we show it is possible to detect and generate images based on psychological properties, like personal taste,” Spapé explains.
Ultimately, the study may benefit society by advancing the capacity for computers to learn and increasingly understand subjective preferences, through interaction between AI solutions and brain-computer interfaces.
“If this is possible in something that is as personal and subjective as attractiveness, we may also be able to look into other cognitive functions such as perception and decision-making. Potentially, we might gear the device towards identifying stereotypes or implicit bias and better understand individual differences,” says Spapé.
There are some serious concerns about this technology as well. Can it be used to unduly influence or steer people towards undesired outcomes. By using deepfake technology, people can interact online with faces that they find attractive and therefore can influence them to do things that they might not other do. Likewise, will it expose personal bias against people that we consider to be unattractive? How will it impact hiring decisions? How will work colleagues collaborate effectively when the AI can determine such deep personal preferences?
“There is no place for the state in the bedrooms of the nation”Pierre Elliot Trudeau
This famous quote from over 70 years ago, from then-Justice Minister Pierre Trudeau when he introduced modernizing reforms to the Canadian Criminal Code in 1967 that decriminalized homosexual acts.
Trudeau’s phrase captured the zeitgeist of a new, more permissive era. It was a harbinger of progressive Canadian thinking on subjects of “morality” through subsequent years, and its sentiment runs through the creation of the Charter of Rights and Freedoms in the early 1980s and the embrace of gay marriage in the early years of the 21st Century.
Now, it may be appropriate the rephrase this quote to say. “There is no place for the state in the minds of the nation”.
Citizens generally feel positive about government use of AI, but the level of support varies widely by use case, and many remain hesitant. Citizens expressed a positive net perception of all 13 potential use cases covered in the survey, except decision making in the justice system. For example, 51% of respondents disagreed with using AI to determine innocence or guilt in a criminal trial, and 46% disagreed with its use for making parole decisions. While AI can in theory reduce subjectivity in such decisions, there are still legitimate concerns about the potential for algorithmic error or bias. Furthermore, algorithms cannot truly understand the extenuating circumstances and contextual information that many people believe should be weighed as part of these decisions.
So, if the public has hesitation with the application of AI by the government in these use cases, how will the public respond if there is a potential to subliminally influence them with presentation of attractiveness that the subconscious finds desirable and then is applied to coerce the public to act or react in a prescriptive manner? This is indeed a slippy slope to climb.
And, it is not just governments that we should worry about, what about marketers using this technology to sell us stuff? Can we be teased into a purchase that we might otherwise not make if the seller is someone that we are attracted too? Personal attraction can drive or affect other human characteristics such as trust and respect. When we are attracted to someone, we want to trust them. But, if these tools are applied against us by clever marketers, that trust is not actually warranted.
Carrasco, M., Mills, S., Whybrew, A,. and Jura, A. (2019). The Citizen’s Perspective on the Use of AI in Government. Boston Consulting Group. Retrieved on March 22, 2021 from, https://www.bcg.com/en-ca/publications/2019/citizen-perspective-use-artificial-intelligence-government-digital-benchmarking
M. Spape, K. Davis, L. Kangassalo, N. Ravaja, Z. Sovijarvi-Spape and T. Ruotsalo, “Brain-computer interface for generating personally attractive images,” in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2021.3059043.
Unknown. (2021). Beauty is in the brain of the beholder: An AI generates personally attractive images by reading brain data. University of Helsinki. Retrieved on March 22, 2021 from, https://www2.helsinki.fi/en/news/data-science-news/beauty-is-in-the-brain-of-the-beholder-an-ai-generates-personally-attractive-images-by-reading-brain-data
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 35 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was senior executive consultant for 15 years with IBM, where he has worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX). Martin currently serves on the Board of Directors for TeraGo Inc (TGO: TSX) and previously served on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now OntarioTech University] and on the Board of Advisers of five different Colleges in Ontario. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section. He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and five certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has earned 20 badges in next generation MOOC continuous education in IoT, Cloud, AI and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, and more.