This opinion paper from researchers at Bern University of Applied Sciences argues that the technology singularity myth is distorting the research agenda in AI and robotics, leading to an overemphasis on human replacement rather than human augmentation. The authors contend that artificial general intelligence (AGI) remains in its infancy, and that robots and narrow AI will never be fully autonomous because human-defined intentions are always required. They highlight how studies predicting massive job losses from automation often overestimate the threat by analyzing individual tasks rather than complex jobs in their entirety, and note that empirical evidence from 1995–2015 shows automation had a positive impact on employment in European manufacturing.
The paper introduces a taxonomy of conjoined agency between humans and machines, distinguishing between interactions that empower workers and those that mechanize them. When humans retain agency over action selection, technology serves as a tool for empowerment; when that agency shifts to the machine, workers risk becoming mechanized — reduced to serving the technology rather than being enhanced by it. The authors advocate for an augmented intelligence approach, where AI supports human decision-making rather than replacing it, while cautioning about challenges like algorithmic discrimination, lack of explainability, and power asymmetries in AI-based worker management systems.
The paper concludes by calling for a shift from technocentric to anthropocentric manufacturing approaches, where collaborative robotics and AI are deployed as tools to enhance worker capabilities, prevent errors, and boost productivity. Drawing on manufacturing philosophies like Kaizen and Agile, the authors stress that the greatest performance gains come from leveraging human-machine complementarity — combining human flexibility with machine efficiency — rather than pursuing full automation.
This review article from Frontiers in Artificial Intelligence examines the intersection of AI deployment, privacy protection, and ethical standards. The authors explore how algorithmic techniques — specifically differential privacy, homomorphic encryption, and federated learning — can serve as effective tools for safeguarding individual privacy while preserving the utility of AI systems. The paper uses the Bletchley Summit as a case study, where 28 governments signed the Bletchley Declaration acknowledging AI's potentially catastrophic risks, and examines the tensions between the US and UK over leadership in AI regulation, including the establishment of the American AI Safety Institute within NIST.
Beyond privacy-preserving technologies, the study addresses broader ethical considerations including algorithmic bias, transparency, and accountability in AI decision-making. It discusses techniques like permutation importance, SHAP values, and LIME for improving model interpretability. The paper also examines AI deployment challenges across sensitive domains such as healthcare, finance, criminal justice, defence, and human resources, noting the need for compliance with frameworks like GDPR, HIPAA, and MiFID II.
The authors conclude that a comprehensive, multidisciplinary approach combining technological innovation with ethical and regulatory strategies is essential for responsible AI deployment. They call for "Ethics by Design" — integrating ethical considerations into the development phase rather than treating them as afterthoughts — and highlight future research needs including longitudinal assessment of AI governance models and the impact of quantum computing on current encryption methods.
