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Navigating Ethical AI Challenges in the Industrial Sector: Balancing Innovation and Responsibility

arXiv 2601.09351 ↗First posted 14 Jan 2026cs.CY cs.AIcited by 0

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Abstract (arXiv)

The integration of artificial intelligence (AI) into the industrial sector has not only driven innovation but also expanded the ethical landscape, necessitating a reevaluation of principles governing technology and its applications and awareness in research and development of industrial AI solutions. This chapter explores how AI-empowered industrial innovation inherently intersects with ethics, as advancements in AI introduce new challenges related to transparency, accountability, and fairness. In the chapter, we then examine the ethical aspects of several examples of AI manifestation in industrial use cases and associated factors such as ethical practices in the research and development process and data sharing. With the progress of ethical industrial AI solutions, we emphasize the importance of embedding ethical principles into industrial AI systems and its potential to inspire technological breakthroughs and foster trust among stakeholders. This chapter also offers actionable insights to guide industrial research and development toward a future where AI serves as an enabler for ethical and responsible industrial progress as well as a more inclusive industrial ecosystem.

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References · 8

  1. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  2. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  3. 2021Compute and Energy Consumption Trends in Deep Learning Inferencecited by 61
  4. 2023Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AIcited by 17
  5. 2023TinyML: Tools, Applications, Challenges, and Future Research Directionscited by 14
  6. 2021Trusted Artificial Intelligence: Towards Certification of Machine Learning Applicationscited by 13
  7. 2023ChatGPT for PLC/DCS Control Logic Generationcited by 9
  8. 2024Machine Learning with Physics Knowledge for Prediction: A Surveycited by 6

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