Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents
Gabriele La Malfa, Lakmal Meegahapola, Edyta Bogucka, Jie M. Zhang, Michael Luck, Elizabeth Black, Daniele Quercia
cs.AI, cs.MA
Submitted: 2026-08-16
Updated: 2026-08-18
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
- Emergence in Multi-Agent Systems: A Safety Perspective
- Foundational Challenges in Assuring Alignment and Safety of Large Language Models
- AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms
- Can Large Language Models Be an Alternative to Human Evaluations?
- TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
- How will Language Modelers like ChatGPT Affect Occupations and Industries?
- Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
- A Safety and Security Framework for Real-World Agentic Systems
- Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions
- Multi-Agent Risks from Advanced AI
- Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions
- With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems
- A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures
- Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks
- From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
- LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
- A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents
- Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
- Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems