Core Viewpoint - The invention of AI large models presents unprecedented opportunities and risks for enterprises, necessitating a collaborative approach between humans and AI to leverage strengths and mitigate weaknesses [3][17][18]. Group 1: AI Development and Adoption Challenges - The rapid development of AI large models has led to capabilities that match or exceed human intelligence, yet over 95% of enterprises fail in pilot applications of AI [3][4]. - The difficulty in utilizing AI large models stems from the need to balance the benefits of efficiency with the costs and risks associated with their application [4]. Group 2: Types of Risks - AI risks can be categorized into macro risks, which involve broader societal implications, and micro risks, which are specific to enterprise deployment [4]. - Micro risks include: - Hallucination issues, where models generate plausible but incorrect or fabricated content due to inherent characteristics of their statistical mechanisms [5]. - Output safety and value alignment challenges, where models may produce inappropriate or harmful content that could damage brand reputation [6]. - Privacy and data compliance risks, where sensitive information may be inadvertently shared or leaked during interactions with third-party models [6]. - Explainability challenges, as the decision-making processes of large models are often opaque, complicating accountability in high-stakes environments [6]. Group 3: Mitigation Strategies - Enterprises can address these risks through two main approaches: - Developers should enhance model performance to reduce hallucinations, ensure value alignment, protect privacy, and improve explainability [8]. - Enterprises should implement governance at the application level, utilizing tools like prompt engineering, retrieval-augmented generation (RAG), content filters, and explainable AI (XAI) [8]. Group 4: Practical Applications and Management - Enterprises can treat AI models as new digital employees, applying management strategies similar to those used for human staff to mitigate risks [11]. - For hallucination issues, enterprises should ensure that AI has access to reliable data and establish clear task boundaries [12]. - To manage output safety, enterprises can create guidelines and training for AI, similar to employee handbooks, and implement content filters [12]. - For privacy risks, enterprises should enforce strict data access protocols and consider private deployment options for sensitive data [13]. - To enhance explainability, enterprises can require models to outline their reasoning processes, aiding in understanding decision-making [14]. Group 5: Accountability and Responsibility - Unlike human employees, AI models cannot be held accountable for errors, placing responsibility on human operators and decision-makers [16]. - Clear accountability frameworks should be established to ensure that the deployment and outcomes of AI applications are linked to specific individuals or teams [16].