Creatures of artifice : Rodney Brooks and the bioethics of animated machines
Bibliographic record
Abstract
Renowned robotics engineer Rodney Brooks has built a career engineering behaviourally intelligent machines for scientific research, military-industrial applications, and domestic service. Drawing lessons from biology and ethology, Brooks designs embodied, responsive robots that he provocatively calls "artificial creatures." He has also been vocal about the broad implications his research carries for the future, making bold predictions about a technological society increasingly shaped by ecologies of animated machines. This dissertation examines a number of popular and academic texts in which Brooks discusses his artificial creatures, his design methodology, and his futurological speculations. Focusing on key moments from these texts, I discuss how he constructs a rhetorical and narrative framework through which he ascribes a sense of "life" to his robots in order to probe the distinction between the living and the nonliving and deliberately unsettle the bounds of the biological and the technological. As he highlights the lifelike qualities of his robots that raise them to the status of creatures, he simultaneously emphasizes the machine-like qualities of human beings, leading him to charge people with "overanthropomorphizing" themselves. I argue that these contrapuntal shifts call into question models of subjectivity derived from modern liberal humanism, insofar as they destabilize traditional relations between machines, animals, and human beings. In order to develop the broader theoretical implications of Brooks' work, I engage in comparative readings that place him in dialogue with philosophers such as Martin Heidegger, Bernard Stiegler, Jacques Derrida, and René Descartes, early cyberneticists Norbert Wiener and W. Grey Walter, and an offbeat video game called Chibi Robo! These readings afford opportunities to challenge modes of thinking and acting that assume human mastery over nature and technology, and subsequently to reevaluate our intimate connections to nonhuman beings that make human life livable in the first place. Ultimately, I endeavour to lay the groundwork for a bioethics that is responsive to redefinitions of life by technological means, one that eschews anthropocentrism in order to suggest a concern for different ways of living and belonging between humans and nonhumans, rather than for the lives of human beings alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".