This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinnings and practical implementations of RLHF techniques, revealing significant limitations in their approach to capturing the complexities of human ethics, and contributing to AI safety. We highlight tensions inherent in the goals of RLHF, as captured in the HHH principle (helpful, harmless and honest). In addition, we discuss ethically-relevant issues that tend to be neglected in discussions about alignment and RLHF, among which the trade-offs between user-friendliness and deception, flexibility and interpretability, and system safety. We offer an alternative vision for AI safety and ethics which positions RLHF approaches within a broader context of comprehensive design across institutions, processes and technological systems, and suggest the establishment of AI safety as a sociotechnical discipline that is open to the normative and political dimensions of artificial intelligence.

Machine Learning Operations (MLOps) involves software development practices for Machine Learning (ML), including data management, preprocessing, model training, deployment, and monitoring. While MLOps have received significant interest, much less work has been published addressing MLOps in industrial production settings lately, particularly if solutions are not cloud-based. This article addresses this shortcoming based on our and our partner’s real industrial experience in various projects. While there is a broad range of challenges for MLOps in cyber-physical production systems (CPPS), we focus on those related to data, models, and operations as we assume these will directly benefit the reader and provide solutions such as lightweight integration, integration of domain knowledge, periodic calibration, and interactive interfaces. In this way, we want to support practitioners in setting up industrial MLOps environments in CPPS. Further, we discuss explainability as an additional part of MLOps, which should be explored in more detail in the future.