Historically, junior professionals have mentored senior professionals around new technologies, because juniors are typically more willing than seniors to perform lower-level tasks to learn new skills, better able than seniors to engage in real-time experimentation close to the work itself, and more willing than seniors to learn innovative methods that conflict with traditional identities and norms. However, we know little about what happens when emerging technologies have a high level of uncertainty in their use, because they have wide-ranging capabilities and are exponentially changing. With the rise of Artificial Intelligence, specifically learning algorithms and LLMs, such contexts may be increasingly common. In our study conducted with the Boston Consulting Group, a global management consulting firm, we interviewed 78 junior consultants in July–August 2023 who had recently participated in a field experiment that gave them access for the first time to generative AI (GPT-4) for a strategic business problem solving task. Drawing from junior professionals' in situ reflections soon after the experiment, we found that junior professionals may fail to manage risks around uncertain emerging technologies because juniors are likely to recommend three kinds of novice risk work tactics that: 1) are grounded in a lack of deep understanding of technologies that have uncertain and wide-ranging capabilities and are changing exponentially, 2) focus on change to human routines rather than system design, and 3) focus on interventions at the project-level rather than system deployer- or ecosystem-level. The implications of novice risk work are that, when junior professionals are expected to be a source of expertise in the use of uncertain, emerging technologies, this can lead to learning failures. This study contributes to our understanding of occupational learning around emerging technologies, risk work in organizations, and human-computer interaction.

中文翻译：

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新手风险工作：初级学生指导高级学生使用生成式 AI 等新兴技术如何导致学习失败


从历史上看，初级专业人员会围绕新技术指导高级专业人员，因为初级专业人员通常比高级专业人员更愿意执行较低级别的任务来学习新技能，比高级专业人员更能够在工作本身附近进行实时实验，并且比高级专业人员更愿意学习与传统身份和规范相冲突的创新方法。然而，我们对新兴技术在使用中具有高度不确定性时会发生什么知之甚少，因为它们具有广泛的功能并且正在呈指数级变化。随着人工智能的兴起，特别是学习算法和 LLMs，这样的环境可能越来越普遍。在我们与全球管理咨询公司波士顿咨询集团进行的研究中，我们在 2023 年 7 月至 8 月采访了 78 名初级顾问，他们最近参加了一项现场实验，该实验让他们首次使用生成式人工智能 （GPT-4） 来执行战略业务问题解决任务。从初级专业人员在实验后不久的现场反思中，我们发现初级专业人员可能无法管理不确定的新兴技术的风险，因为初级专业人员可能会推荐三种新手风险工作策略，即：1） 基于对具有不确定性和广泛能力且呈指数级变化的技术缺乏深刻理解， 2） 关注人类日常工作的改变，而不是系统设计，以及 3） 关注项目层面的干预，而不是系统部署者或生态系统层面的干预。 新手风险工作的含义是，当初级专业人员被期望成为使用不确定的新兴技术的专业知识来源时，这可能会导致学习失败。本研究有助于我们理解围绕新兴技术的职业学习、组织中的风险工作以及人机交互。