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TRUVACE RECORD VERSION
record: TRV-2026-0411
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T10:34:49.918177Z
status: published
lens: p_space
sector: labor
headline: Artificial intelligence in industry – ethical challenges
dek: Abstract Artificial intelligence is revolutionising industry, significantly improving work efficiency. However, the development of AI continues to generate new ethical challenges. An international legal framework has already been developed to ensure the ethical and safe development of AI. However, it seems that new threats and ethical dilemmas continue to arise as AI develops further. This study aims to explain some of the benefits and threats of using AI in industry and to encourage reflection on the need to in…
gain_title: (none)
problem_title: Industrial AI deployment creates ongoing ethical threats linked to entrusting machines with autonomy and decision-making responsibility.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Industrial AI deployment creates ongoing ethical threats linked to entrusting machines with autonomy and decision-making responsibility.
problem_evidence: new threats and ethical dilemmas continue to arise as AI develops further | lack of consideration of the dangers associated with the responsibility involved in entrusting machines with autonomy and decision-making
quick_read: Published December 2025 in Production Engineering Archives, this peer-reviewed theoretical paper reviews how artificial intelligence is being used in industry, including cobots, algorithmic management, employee monitoring, sustainability efforts, and generative AI, and summarizes existing international legal frameworks for safe and ethical AI.

The review matters for workers and managers because it highlights that efficiency gains coexist with unresolved questions about who bears responsibility when autonomous systems make decisions, and it calls for intensified ethical reflection as AI capabilities advance, though as a literature-based analysis it does not provide new empirical data on workplace impacts.
limitation: The analysis is theoretical and literature-based without empirical measurement of outcomes, limiting generalizability to specific workplaces.
tag: Evidence-backed problem
key_points: Study examines selected industrial uses including cobots, algorithmic management, employee monitoring, sustainable development implementation, and generative AI. | Article reviews existing international legal frameworks developed to ensure ethical and safe AI development. | Authors note persistent gap in addressing responsibility when entrusting machines with autonomy and decision-making.
rundown: The paper surveys industrial AI applications such as cobots, algorithmic management, employee monitoring, sustainable development initiatives, and generative AI, noting improved work efficiency alongside emerging risks.

It describes international legal frameworks intended to ensure ethical and safe AI development, but argues that new threats continue to emerge and that responsibility for autonomous machine decision-making remains insufficiently addressed.
sources:
- peer_reviewed | Production Engineering Archives | https://doi.org/10.30657/pea.2025.31.51 | 2025-12-01
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9f88278b1a4c14fd176e28100faa7463ec0af3c5a396ab260822972b0712110a

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