Abstract: Facial recognition turns faces into numbers and data. When faces become numbers and data, facial recognition is capable of much more than just recognizing faces.
Facial recognition makes it possible to identify an offender before the criminal is convicted. In this way data can foil a crime by apprehending and convicting the criminal even before the crime is committed. Using data in this fashion, I call ‘cyberphrenology’.
The concept of ‘cyberphrenology’ include an ambition to master the future. The precognitive element in the way we use data is motivated by an unrealistic political agenda.
Politicians demand that law enforcement must stop crime. This political prioritization requires a transition in which law enforcement resources become focused towards datadriven surveillance.
The Danish government’s recently amended security legislation suggests that the transition has occurred. Privacy is traded in for datadriven security.
I. MAGIC MIRROR ON THE WALL…
Digital facial recognition is a somewhat fluffy term for technologies that recognize faces. It sounds simple and innocent. Technology transforms faces into numbers and data. Simsalabim.
But when faces are turned into numbers and data, facial recognition is capable of much more than just recognizing faces.
Since the dawn of time, we have acknowledged that beauty lies in the eyes of the beholder. Those days are over. When technology transforms faces into data and numbers, algorithms can determine who is ‘the fairest of them all’ – and who is not. Just ask the numbers in the magic mirror. Then we don’t waste time defining beauty. It’s called efficiency. But data about your face can do much more than determine if you are pretty or not.
Since antiquity, we have been of the conviction that facial features reflect unique personal traits. A pseudoscientific belief that there is a connection between body and soul. That faith is called phrenology.
Phrenology was created by Austrian-German physician Franz Joseph Gall (1758-1828) and his thesis is based on the claim that different organs in the human brain represent different properties and that these properties can be measured on the surface of the skull. Gall believed he had invented a method by which he could measure character, personality, intelligence and psyche by examining the skull’s external shape. When facial recognition technologies translate faces into numbers, it becomes possible to measure and digitally remaster a relationship between personality and looks.
Facial recognition will recognize you, and decide whether you are pretty or not. But from a phrenological perspective, data is also able to determine, if you have a good or bad character.
When numbers from facial recognition are used in a correlation with other numbers, data can be used to establish whether you are worthy or a risk; whether you are privileged or vulnerable. Ultimately, data can be used to choose whether you are in or out. Using data in this way, I call cyberphrenology.
Cyberphrenology is phrenology in a digital disguise. Despite the prejudicial and racist conceptual heritage, cyberphrenology represents a new datadriven way of viewing the world. We change everything into numbers. Even faces. Numbers don’t lie. They can’t. Lying is a human trait. Yet, numbers don’t always tell the whole truth. Still, many decisions are based on faith in numbers. The belief in data transform any qualified guess or assumption into eternal, final truths. The cyberphrenological paradigm disrupt our cognitive bias – changing attitudes and opinions about each other.
Cyberphrenology is like looking at others through a kaleidoscope where you see the person but also see an interpretation of the data associated with that person. Facial recognition not only recognize your face, but place your face on the data-set that is precisely attributable to you. Facial recognition will recognize you in a way, you can’t recognize yourself.
IV. THE BORN CRIMINAL
Based on the principles of phrenology, ‘the father of criminology’, Cesare Lombroso (1835-1909) claimed that criminal behaviour was hereditary and innate. ‘The born criminal’ could be identified through studies and measurements of physical features and Lombroso performed a number of anthropometric studies focusing on the measurement of skulls.
Lombroso’s anthropometric profiling of criminals had the intent to validate the claim that measurement of skulls makes it possible to identify persons with criminal inclinations. From a criminological point of view, Lombroso’s idea of ’the born criminal’ has been rejected as pseudoscientific. Yet facial recognition technology breathes new life into Galls and Lombroso’s discriminatory and biased claims.
In 2016, two researchers from Shanghai Jiao Tong University, Xiaolin Wu and Xi Zhang, developed an AI based on photographic facial recognition. Their claim is that with their algorithm, they can teach a machine to read images of human faces and determine whether the depicted person has a criminal character. Their research provides evidence that their AI – with 89.5% accuracy – can determine whether a person is a convicted criminal or has criminal traits. Facial recognition can determine whether you have a law-abiding or a criminal character.
The argument is that facial recognition technology can identify a criminal even before the offender is convicted. In theory, facial recognition can foil a crime by apprehending and convicting the offender before the crime is committed.
Cyberphrenology includes an ambition to master the future. Facial recognition is a data-driven crystal ball. The precognitive element of the way we use data is the main motivation and driving force of data-driven surveillance.
Data from facial recognition can document your past and examine your present. But the goal is to predict your future – with tangible mathematical certainty. Personal data from facial recognition can predict your future and give predictions a hint of evidence-based scientific objectivity.
The technology produces biases, prejudices or predictions about you that you may find difficult to get rid of. Data has no ‘Sell-by-date’. Data can confront you with your past and present at any point in your future.
The precognitive element of the way we use data is motivated by an unrealistic political agenda. Politicians demand that law enforcement must stop crime before it occurs. On that account, we must expect the police will be required to down-size on preventative and investigative efforts to shift resources into data-driven intelligence surveillance. The Danish government’s recently amended security legislation suggests that the transition already has taken place.
VI. SECURITY PACKAGE
Offending is not rooted in genetics. People commit crime as a result of situational or rational choices. Still, the precognitive element of cyberphrenology, which is the focal point of the government’s “Security Package”, suggests an uncritical return to the idea of the “born criminal”: a belief that data from surveillance and facial recognition can predict human behaviour in the future. Thus, the political intent in the “Security Package” dismisses a century of criminological theories on why people commit crime. More surveillance does not resolve the basic circumstances that are generally considered to be breeding grounds for crime in contemporary criminological theories.
However, from a policing perspective, facial recognition is a formidable tool for investigating crimes. The technology offers a promise of more effective police, prosecution and control. Police and courts are increasingly using data-driven evidence derived from mass surveillance where public and private data sources are interrelated.
Use of facial recognition data has an impact on established methods of evidence, changes legal norms, and makes it possible to punish crime that has not yet been committed.
The prevailing political assumption is that increased surveillance, control and harsher penalties provide increased reassurance against becoming victim of crime. Security has become data-driven.
VII: SECURITY OR PRIVACY
In general, data-driven democracy involves an opaque exchange of rights where a number of ethical, legal and human rights issues become stretched beyond recognition. Datadriven security challenges citizens’ freedoms, including the right to privacy.
Rights to privacy and personal freedom are exchanged in a trade-off, where security concerns outweigh the values of democratic rights. Digital technology disrupt legislation and human rights, but we still claim to live in a democracy. A data-driven democracy. A democracy without democratic control.
Facial recognition is pushing the boundaries of privacy violations. A quick glance at any crystal ball suggests that future democratic rights will be under siege from exponential technological developments.
The challenge is that we have only just begun to realize the change. Resolving a problem is conditioned upon acknowledging that you have a problem.
Are you willing to trade privacy for datadriven security?