Robo-advisors (RAs): the programmed self-service market for professional advice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This conceptual paper draws together an interdisciplinary approach to robo-advisors (RAs) as an example of an early and successful example of automated, programmed professional services. Design/methodology/approach Little is known about the forces driving this change in the delivery of professional service. This work explores the drivers of RAs, the degree of disruption incurred by the introduction of RAs, and how, as RAs advance, trust in algorithmic authority aids in legitimating RAs as smart information. Findings From the firms' perspective, the drivers include rebranding occasioned by the financial crisis (2008), the widening of the client base and the “on-trend” nature of algorithmic authority guided by artificial intelligence (AI) embedded in RAs. This examination of the drivers of RAs indicates that professional service automation is aligned with information society trends and is likely to expand. Practical implications Examining RAs as an indicator of the future introduction of programmed professional services suggests that success increases when the algorithmic authority in the programmed serves are minimally disruptive, trustworthy and expand the client base while keeping the knowledge domain of the profession under control of the industry. Originality/value Treating RAs as an early instance of successfully embedding knowledge in AI and algorithmically based platforms adds to the early stages of theory and practice in the monetization and automation of professional knowledge-based services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it