Learning from Machines: Conversations with Artists about Machine Learning

Introduction

Machine learning (ML) is said to be changing the nature of what it means to be human [2], but the human is simultaneously being decentred [3]. According to Matteo Pasquinelli, any machine is a machine of cognition, and such machines enable new forms of logic [4]. Conversely, Dan McQuillan regards AI as lacking cognition in a human sense: “It is simply savant at scale, a narrow and limited form of ‘intelligence’ that only provides intelligence in the military sense, that is, targeting.” [5] Should we question machine learning as a technology of control, or embrace its new forms of meaning making?

The Trouble with Learning

Machine learning typically conceives of learning as the training of an individual learner. It can be considered ‘social’ in that the learner typically receives a set of training data from some external source, the data being labeled by a human (in supervised learning) or not (in unsupervised). The learner may make inductive inferences or receive reinforcement from an external source such as a domain expert or via crowdsourcing. Feedback may come from the programmer or system designer, from crowdsourcing, or as part of this training process. But ML is not generally seen as an inherently social process. Active learning [13] and interactive ML [14] involve frequent feedback mechanisms, though they differ depending how and from whom feedback is solicited [15]. Both may involve co-learning — such a ‘human-centred’ approach conceives of ML as augmentation, not automation [13], with learning seen to apply to the human as well as the machine in the system.

Figure 1. Conversational Framework for learning © Diana Laurillard, 2002. Used with permission.

What is ‘Learning’ in Machine Learning?

The conceptualisation of ‘“’learning machines‘”’ by Pask was based on work done in the 1950s on early cybernetic devices such as the ‘perceptron’ [34]. Cybernetics locates learning in systems, whether human or nonhuman and operating at different scales. Snaza et al [12] point out that the ‘cybernetic triangle’ of human/animal/machine is made up of fabricated borders.

Fall of the House of Usher (2017) by Anna Ridler. Used with permission.

Where is the Machine?

If a different form of learning arises when the statistical inferences of, say, a neural net, meet human language and subjectivities, what and where is the machine in ML, given the proliferation of methods and devices?

Figure 3: The Monistic Almanac (2018) by David Benqué. Used with permission.
Gymnopaedia App by Erik Lintunen. Used with permission.
Figure 4: Heaven to the Cloud (2019) by Jesse Cahn-Thompson. Used with permission.

Learning as Conversation

In Laurillard’s conversational framework, as for Pask before her, shared understanding is the goal, and conversation is the means to achieve it. Specifically, the teacher has (or builds) a model which is compared with a learner’s. “They’ve got to reach agreement,” she told me, “or generate a better thing together than they would separately.”

The Level of Descriptions

The top level of Laurillard’s conversational framework is the Level of Descriptions, in which teacher and learner articulate their conceptions with an aim to come to agreement through iterative evaluation and feedback.

The Level of Actions

We have seen how language can prompt action in ML — most obviously in programming code as a set of instructions, but conversely when the computer encounters an error and prompts (or instructs) the human operator to address it before continuing.

Observation

Observation is central to cybernetics, with an observer situated either outside or as part of a system. By extension in Pask’s conversation theory, conversants observe each other’s actions, and there may be an additional observer. Central to the humanist notion of objectivity is empirical observation; Mcquillan calls this “onlooker consciousness,” wherein a supposedly outside observer not only observes but manipulates an experimental condition [5]. Quantum theory, as described by Barad [79], and second-order cybernetics both support this, situating the observer as part of the system being observed.

Machine Learning as Art, not Science

Ridler is concerned that most of the research being done on ML assumes access to many more processors than the two she relies on. “It’s starting to lock out experimentation from people who are not affiliated with [big companies],” she said. Mackenzie notes that many of the brightest computer scientists have been incentivised to work on ML algorithms for optimisation of online advertising. Cahn-Thompson pointed out that writing code is often abstracted from its end use. Here, then, is another hidden layer, that of commercial exploitation. According to Mcquillan, “The opacity of machine learning is not only that of the black box. It is also a consequence of algorithms hidden behind the high walls of commercial secrecy“ [5].

Conclusion

Is the ‘machine’ all of us? “The way out of a machinic metaphysics that eludes accountability,” according to Mcquillan, “is to find a form of operating that takes embodied responsibility. Moreover, this embodiment should start at the ‘edges’.” He draws from Barad in suggesting that a counterculture of data science “can be a critique that also becomes its own practice” [5], Based on my conversations, recasting ML as a critical pedagogy means exposing hidden layers, but also accepting the ways of understanding particular to every participant, while maintaining a degree of control and an ethical position.

References

[1] Erik Lintunen, Latent Spaces: Construction of Meaning and Knowledge through New Media, Art, and Artificial Neural Networks, p.10. (MA dissertation, Royal College of Art, 2019)

Acknowledgements

Thanks to the artists, Diana Laurillard, Eleanor Dare, the 2019 SIGCHI workshop on Human-Centred Machine Learning, and various peer reviewers.

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Artist & researcher. http://increasinglyunclear.world

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