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Record W4405004574 · doi:10.3233/atde240919

The Use of AI and Robotics in Armed Conflicts

2024· book-chapter· en· W4405004574 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in transdisciplinary engineering · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVariety (cybernetics)Engineering ethicsRoboticsPerspective (graphical)Political scienceArmed conflictRelation (database)Subject (documents)Artificial intelligenceSociologyManagement scienceEngineeringLawComputer scienceRobotLibrary science

Abstract

fetched live from OpenAlex

This systematic literature review (SLR) explores existing and newly emergent ethical and legal challenges associated with the use of AI and robotics in armed conflicts. We conducted an extensive review of relevant scholarly publications associated with (lethal) autonomous weapons systems (LAWS). Besides the ethical and legal principles, we also explore emergent technical applications associated with these technologies in armed conflict(s). Our particular focus is to compare literature from the last 12 years with publications since the outbreaks of recent armed conflicts from the perspective of LAWS. We engage in exploring and identifying the shifts in ethical arguments and discourse, as well as shifts in policy subject themes, and standards setting around the use of emergent technology in relation with AI and robotics. Our contribution analyses emergent socio-technical themes and arguments relevant for engineers, policy-makers, and other interdisciplinary scholars across a variety of disciplines.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.337
Teacher spread0.293 · 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