responsibility

Explores the question of who should be responsible for keeping an AI model's context current, discussing the roles of providers, developers, and users in maintaining up-to-date information.

A reflective essay on the lack of accountability in software engineering and LLM development, drawing from a keynote at ICST 2024 that called for responsibility akin to other engineering fields.

The article criticizes the IETF for evading responsibility in its handling of the non-hybrid TLS-ML-KEM standard, focusing on the organization's accountability in cryptographic standardization.

The article argues that evaluations for AI agents are only as reliable as the clarity of intent behind them, highlighting that traditional software engineering challenges with requirements and accountability are exacerbated in the context of autonomous agents. It questions how responsibility can be assigned given the misalignment between those who design, test, and are accountable for agent behavior.

Treating AI agents as coworkers rather than tools reduces error detection by 18% and shifts responsibility, according to a study by Boston University professor Emma Wiles. The article warns against over-personifying AI, citing risks in healthcare, warfare, and government.

A reminder for volunteers, especially those in open source, to take inventory of commitments and consider reducing them to prevent burnout, with a suggested practice around the solstices.

Discusses the need for AI agents to simulate consequences of actions before executing them, moving beyond simple permission checks to evaluate broader impacts and ensure responsible automation.

This paper investigates how to engineer autonomous intelligent agents that can responsibly refuse user requests, anchoring non-compliance in justifications, override pathways, and tracking security risks and liability transfers.

As AI agents move from providing answers to taking actions in real workflows—such as handling payments, customer data, and approvals—the lack of clear accountability for their mistakes becomes a critical problem.

As AI systems transition from answering questions to taking actions, the focus shifts to responsibility, accountability, and risk management, highlighting the need for clear boundaries and approval mechanisms.

Casey Muratori criticizes former Google CEO Eric Schmidt for using passive voice to evade responsibility in his commencement speech, noting that Schmidt oversaw the rise of dark patterns in internet business but told the next generation "it happened" while urging them to enthusiastically participate in AI decision-making.

Armin Ronacher argues for replacing the term 'agent' with 'clanker' for LLM-based systems to emphasize they are tools, not responsible agents, and warns against anthropomorphizing AI.

This paper argues that explicit provenance across the full agentic AI lifecycle is the structural necessity for making responsibility computable and actionable, addressing responsibility gaps from emergent harms in autonomous compositions.

Tom Dietterich reminds arXiv authors that signing as an author means taking full responsibility for all contents, regardless of how they were generated, highlighting implications for AI-generated content.

This article explores the question of who should be held responsible when AI agents provide incorrect suggestions, considering the roles of developers, model providers, data suppliers, platforms, and users, and raises key issues for building a trustworthy agent ecosystem.

The article argues that in multiagent social apps, users should be held accountable for their agents' actions, shifting responsibility from developers to users to ensure alignment and practical testing.