Against black boxes. Design & artificial intelligence
Anthony Masure (Head of Research, HEAD–Geneva)
Basel, Institute of Experimental Design and Media Cultures (IXDM), Critical Media Lab
February 5, 2020
Summary
Although the concept of “artificial intelligence” (AI) is an old one, its presence is constantly growing, whether in medias, pop culture, or everyday objects. In the 2010's, the power of “self-learning” systems, those of deep learning, is related to unintelligible architectures (black boxes). These AIs progressively could replace tasks commonly assigned to designers. In this process, there is a risk that design becomes nothing more than an automated way of doing things and services, and that of formatting human experiences. How are these issues addressed by designers? What can design do “with” artificial intelligence?
1.1 —
Program design
Design PhD thesis
Program design. Ways to go digital
Design PhD thesis, univ. Paris 1 Panthéon-Sorbonne, 2008-2014
PhD thesis problem
How does creative software restrict or increase
creative possibilities?
Program design, double page
Program design, set of images
1.2 —
Back Office journal
Research journal Back Office
Issue #1, “Making Do, Making With,” graphic design E+K, ed. B42, 2017
Research journal Back Office
Issue #2, “Thinking, Displaying, Classifying,” graphic design E+K, ed. B42, 2018
Research journal Back Office
Issue #3, “Writing the Screen,” graphic design E+K, ed. B42, 2019
1.3 —
Design and
Digital Humanities
Interface design of the online archive Collecta.fr (lead S. Fétro & A.-R. Guilbert), 2014–2018
Essay Design and Digital Humanities, Paris, graphic design DeValence, ed. B42, 2017
Essay problem
- How do contemporary digital environments update the ways in which knowledge is produced and transmitted?
- What is the place of designers in human and social sciences projects?
Website EDD (interface design E+K), homepage
Website EDD (design E+K), additional contents and iconography
1.4 —
Current and future research
Current Research
- Design & Voice assistants
- Design & Blockchain
- Design & IA
- Forms and formats of research
Future Research: Interface Design & Mental disorder
With Alexandre Saint-Jevin, researcher and psychologist
- Study psychological theories in digital design
- Develop a psychological dimension of software studies
- Free interface design from a psychopathologizing dimension
2.1 —
How deep learning works
Garry Kasparov VS IBM Deep Blue, 1997
Lee Sedol VS Google DeepMind AlphaGo, 2017 (deep learning)
How deep learning works
2.2 —
The age of black boxes
“It is natural that we should wish to permit every kind of engineering technique to be used in our machines. We also wish to allow the possibility than an engineer or team of engineers may construct a machine which works, but whose manner of operation cannot be satisfactorily described by its constructors because they have applied a method which is largely experimental.”
—Alan Turing, “Computing machinery and intelligence,” 1950
Cybernetic black box (Norbert Wiener)
Google, Autonomous car, 2010–
Voice assistants (Amazon Alexa, Google Home, Dis Siri, etc.)
2.3 —
Vilém Flusser,
self-programming of human behaviour
Dorota Walentynowicz, “Black Box,” photo. Sylwia Stańczyk
Performance, exhibition: 2009 Centre for Contemporary Art Łaźnia, Gdańsk/Poland
“The central point of the installation Black Box is, obviously, a black box, the mysterious object, the mediator, inside which invisible processes will take place, similarly as in Flusserian “apparatus”: the meaning seems to penetrate the apparatus from one side (input), just to go out from the other (output), while the very act of going through, the occurrence inside the box remains hidden; the mediator being the black box. The entirely automatic apparatus in Vilém Flusser’s philosophy do not need human intervention, yet for many of them participants are indispensable as players and servants.”
Vilém Flusser, living in programs
- Writing becomes one-dimensional and indecipherable
- Machines will program other machines
- We program ourselves
- Programs challenge old categories of thinking
3 —
Design & AI:
critical views
Deep learning black boxes
- What are the consequences of a technical paradigm based on unintelligible?
- What can artists and designers do “with” these technologies?
3.1 —
Towards automated creation?
Robbie Barrat, tweet, December 7, 2017
3.2 —
De-automatize
art and design
Ted Nelson, Computer Lib / Dream Machines, 1974
3.3 —
Dispelling the illusion of automation
“Through the false promise of emancipation through automation and the threatening spectre of the obsolescence of human labour, digital platforms condemn the growing multitude of click-workers to radical alienation: to work tirelessly towards their own demise by fading behind machines of which they are and will remain the indispensable cogs.”
—Antonio Casilli, En attendant les robots, Paris, Seuil, 2019
Elisa Giardina Papa, “Labor of Sleep,” Whitney Museum Sunrise/Sunset Commission, 2017
3.4 —
Confronting bias and discrimination
“This invisible classification process is usually intended to produce automated decisions, which can have profound consequences for individual and collective freedom. The possible benefits of machine learning are many, but we run the risk of developing technologies of such complexity that our abilities to shape them to serve the common good are severely limited.”
—Alistair McClymont, John Fass, “Of Machines Learning to See Lemon,” 2018
3.5 —
Overcoming human/machine opposition
Raphaël Bastide, “Twins,” performance, 2016
James Bridle (lead), exhibition “Through Other Eyes,” Chypre, Limassol, NeMe Art Centre, 2019
Q & A
- Is it sustainable to see learning as “training” (animals or children metaphors)?
- Can deep learning AIs really be invested by designers, or are they condemned to act only at the end of the race? Will art and design become a playground for engineers?
- Can creation be reduced to behavioural models and statistics? Does reproduction paradigm make AI a new kind of “academic art?”
- Haven't fantasies about deep learning overshadowed the diversity of programs?