Novice risk work: How juniors coaching seniors on emerging technologies such as generative AI can lead to learning failures
Bibliographic record
Abstract
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. • Junior professionals may fail to be a source of expertise for seniors in GenAI use. • Juniors' focus on human routines and project-level work fails to manage novel risks. • GenAI risk work must target developers and system deployers in addition to users. • And it must target data, models, and infrastructure in addition to human routines. • GenAI requires system-level intervention for most effective organizational use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".