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Record W3134465806

Animal Expectations: Intelligible Classification of Self-Driving Cars

2018· article· en· W3134465806 on OpenAlexaboutno aff
Ben Wagner, Jon Crowcroft

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsAutopilotSet (abstract data type)AutomationFunction (biology)Self drivingComputer scienceScheme (mathematics)CertificationComputer securityArtificial intelligenceHuman intelligenceRisk analysis (engineering)EngineeringBusinessTransport engineeringControl engineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

One of the main challenges with self-driving cars are the outlandish human expectation associated with them. Part of this challenge refers to the mental models associated with understandings of machine “learning” and artificial “intelligence”, which are typically associated with human learning and intelligence. Another key challenge is related to the way in which these systems are marketed, sold as ‘self-driving systems’ or ‘autopilots’ which misrepresent the actual technical capacities of the system and particularly their ability to function autonomously. To consumers, saying these systems are ‘Level 2’ self-driving systems is meaningless. Thus, we instead propose a mandatory classification scheme based on the animal world to set appropriate expectations of self-driving vehicles. In this scheme, different levels of automation would be associated with similar levels of animal intelligence. This would help set consumer expectations more accurately and ensure an increase in safety while using self-driving cars. It also provides for a fantastic opportunity for community-led certification and generation of appropriate imagery. For example, Level 2 autonomous vehicles could most appropriately be described as being driven by ‘worm intelligence.’ Thus vehicles driving at this level such with a Tesla AutoPilot or a Nissan ProPilot system would require a large sticker stating ‘Tesla AutoPilot brought to you by worm intelligence’ together with the picture of a worm affixed on the outside of the vehicle. Successive levels of automated vehicles could then have different appropriately selected animals associated with them to combat false advertising claims and ensure that consumers have an accurate picture of the capabilities of their vehicles. Additionally, we thus strongly believe that vehicle licensing authorities should provide meme generators as part of the licensing process to ensure that consumers can fully understand their vehicle capabilities. The ability of individuals to playfully reimagine the exact capabilities and failures of their existing vehicle would be a welcome change to the existing assumptions of technological performativity. Only the type of animal would have to be restricted to certain levels - you can’t claim that your caterpillar AI is in fact a Labrador AI – but beyond that anything is possible. However, this raises a separate associated problem around human understandings of animal intelligence. In particular dog owners or cat owners may be likely to overimagine the capabilities of their specific pets to drive cars. Using domesticated animals to represent intelligence is thus likely to lead only to further anthropomorphising of artificial intelligence. As a result, the choice of animals for such certification mechanisms should be restricted to non-typically domesticated animals only. At the same time the ability of dogs should not be underestimated. Some dogs such a Borzoi or Rhodesian Ridgebacks have been trained to hunt wolves or lions in packs. However, nothing in this article should be used to suggest that wolf-hunting dogs should necessarily be driving cars down motorways. Finally regular testing of public reactions to specific animal associations are necessary, as the metaphorical labelling expectation may shift and or change over time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.286
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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