CORTEXA
← Browse
arxivcs.AIcs.CLcs.ETcs.LGcs.MA2026-07-23

The Boundaries of Automation: A Theory of Persistent Human Participation

Fares Fourati, Hinrich Schütze, Eyke Hüllermeier, Iryna Gurevych

The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the assumption that humans remain in the loop only because current AI systems are not yet sufficiently capable. This paper challenges that assumption. Rather than asking how far automation can extend, we ask where its conceptual limits lie and argue that human participation may persist even with highly capable AI systems for three distinct reasons. Technical or complementarity grounds arise when humans contribute capabilities or perspectives unavailable to AI. Normative or developmental grounds arise when participation itself is valuable for human agency or learning. Most importantly, emergence grounds arise from target emergence: in some activities, the target is not fully specified in advance but instead emerges through the interaction itself. In these cases, human participation is not merely a means of improving execution but is constitutive of the target being produced. Human--AI co-construction, understood as the joint production of outcomes by humans and AI systems, is therefore not simply a temporary response to imperfect AI, but a persistent feature of activities whose objectives emerge through participation. This perspective has important implications for the limits of automation and for the design, evaluation, and ethics of future AI systems.

View free PDFSource page

Related papers

arxivcond-mat.dis-nncond-mat.stat-mechcs.AInlin.CD2026-07-24

Multiplicity of Stable Attractors in Disordered Neural Models

Raffaele Marino, Roberto Livi, Antonio Politi

We show how large-deviation statistics allows one to obtain reliable estimates of the multiplicity of stable fixed-points in a model of neural ordinary differential equations previously employed in computational tasks. The result is obtained by developing a suitable perturbative…

View free PDFSource page
arxivcs.AI2026-07-24Cited by 2

Explainable Reinforcement Learning for assisting Air Traffic Controllers

Anduel Mehmeti, Gabriella Gigante, Salvatore Venticinque

To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is…

View free PDFSource page
arxivcs.HCcs.CL2026-07-24

Towards Reducing Foreign Language Anxiety Using Level-Appropriate Embodied Conversational Agents

Krishan Rajaratnam, Wenbin Gan, Yuan Sun

Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be ins…

View free PDFSource page
arxivcs.NIcs.MAeess.SY2026-07-24

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

Chuan-Chi Lai, Ang-Hsun Tsai

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…

View free PDFSource page