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Biometric Mirror

 

Biometric Mirror

What if Artificial Intelligence gets it wrong?

Biometric Mirror questions the accuracy and assumptions of facial recognition algorithms. Enter a sci fi beauty salon and let an AI scan your biometric data and reveal a mathematically ‘perfect’ version of your own face. But whose version of perfection is it really? 

 
Photo – Jesse Marlow

Photo – Jesse Marlow

Lucy McRae AI BIOMETRIC MIRROR
Photo – Jesse Marlow

Photo – Jesse Marlow

Biometric Mirror is an immersive installation that blends the act of casually glancing at one's reflection with modern algorithmic perspectives on facial perfection. The artwork explores the accuracy and flaws of artificial intelligence and the 'uncanny valley' of algorithmic perfection and its potential black mirror outcomes.

Part of Science Gallery Melbourne’s PERFECTION exhibition program, this AI mirror analyses an individual’s character traits based solely on their face – to investigate the way that people respond to AI analysis. The mirror compares its onlooker to a database of faces that have been assessed on 14 characteristics, before issuing them a statement that summarises their ‘attractiveness’ and their emotional state. In theory, the algorithm is correct, but it’s likely the information isn’t – because how can it be if it’s based on subjective information?  This intriguing collaboration is with scientists from the Research Centre for Social Natural User Interfaces, invented by Dr Niels Wouters.

 

“Creating platforms to discuss the cultural implications of emerging technologies, like artificial intelligence means that we can expose any assumptions in a public space." 

Science Gallery's PERFECTION trailer explores the ritualisation of artificial intelligence via a ceremony performed by digital shamans on a test subject. This experiment looks at the uncontrollable imperfections that occur when working with the human body in contrast with the controlled and programmable systems of artificial intelligence. How do we reassess and imagine new algorithmic paradigms that encompass imperfection, accident and messiness? PERFECTION opens in September 2018 at the Melbourne School of Design, the University of Melbourne. Through the lens of artists, musicians, mathematicians, architects, designers, psychologists and surgeons, Science Gallery Melbourne will explore what it means to pursue perfection in a non-perfect world. Commissioned by Science Gallery Melbourne. Director / Producer – Lucy McRae Cinematographer – Shaun Dougherty Photographer / Co-Producer – Jesse Marlow Production Assistant – Ross Bird Steadicam Operator – Grant Sweetman Focus Puller – Ben Bryan Gaffer – Tom Savige, Hannah Location – Five Easey Upstairs Photogrammetry – siii Projects, Ben Waters Human Computer Interaction Researchers – Dr Niels Wouters, Nicholas Smith Motion Graphics Artist – Brendan Ho Art Department – Luke McMahon, Phin Muir, Marcus Shanahan Props Fabrication – Boom Studios Costume Design – Lucy McRae, Steven Swain Facial Harness – Simone Agius, Mick Peel Music – Wayne Joseph Kington Compositor – Dave Abbott Colorist – Tim Wreyford Digital Shamans – Kurt Pimblett, Eamon Dunphye, Benjamin Goss, Caitlin Costello Test Subject – Lucy McRae Special thanks to Arla Marlow, Lindy Dobson and Steven Swain
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As beauty brands continue to embed AI technology into their offering, real people are starting to value the advice of machine learning. But upon what ideals is our ‘beauty’ being judged?

DAZED BEAUTY THE ILLUSION OF PERFECTION: DISTURBING TRUTH ABOUT AI BEAUTY

Biometric Mirror is a moment to start thinking about transparency of algorithms, consenting and deconsenting, and the current trend of perceiving algorithms (and AI) as the holy grail that will ultimately improve society.

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Science Gallery's PERFECTION trailer explores the ritualisation of artificial intelligence via a ceremony performed by digital shamans on a test subject. This experiment looks at the uncontrollable imperfections that occur when working with the human body in contrast with the controlled and programmable systems of artificial intelligence. How do we reassess and imagine new algorithmic paradigms that encompass imperfection, accident and messiness?