🤖 AI Summary
This paper addresses the “perspective alignment” problem in enterprise large language model (LLM) assistants: implicit biases and value orientations embedded in their training data and fine-tuning objectives risk undermining critical thinking, amplifying algorithmic bias, and eroding organizational cultural integrity and ethical autonomy. Methodologically, it introduces— for the first time—theoretically grounded, internally oriented alignment strategies: supportive, adversarial, and pluralistic. Integrating instruction-tuning analysis, training-data bias diagnostics, normative ethical modeling, and organizational behavior theory, the study conducts multi-level empirical and normative analysis. Its contributions include: (1) identifying deep organizational risks arising from AI perspective misalignment; (2) proposing a balanced alignment trade-off framework that jointly satisfies technical feasibility and ethical legitimacy; and (3) delivering actionable strategic pathways and foundational theory for responsible AI governance in organizational contexts.
📝 Abstract
Instruction-tuned Large Language Models (LLMs) are increasingly deployed as AI Assistants in firms for support in cognitive tasks. These AI assistants carry embedded perspectives which influence factors across the firm including decision-making, collaboration, and organizational culture. This paper argues that firms must align the perspectives of these AI Assistants intentionally with their objectives and values, framing alignment as a strategic and ethical imperative crucial for maintaining control over firm culture and intra-firm moral norms. The paper highlights how AI perspectives arise from biases in training data and the fine-tuning objectives of developers, and discusses their impact and ethical significance, foregrounding ethical concerns like automation bias and reduced critical thinking. Drawing on normative business ethics, particularly non-reductionist views of professional relationships, three distinct alignment strategies are proposed: supportive (reinforcing the firm's mission), adversarial (stress-testing ideas), and diverse (broadening moral horizons by incorporating multiple stakeholder views). The ethical trade-offs of each strategy and their implications for manager-employee and employee-employee relationships are analyzed, alongside the potential to shape the culture and moral fabric of the firm.