Collaborating with technology-based autonomous agents
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 article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work. Design/methodology/approach The panelists (Drs Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision-making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis for a moderated panel discussion (the moderators were Drs Isabella Seeber and Lena Waizenegger), during which the initial position statements were elaborated on and additional issues were raised. Findings Through the discussion, a set of additional issues were identified. These issues related to (1) the design of autonomous technology-based agents in terms of human–machine workplace configurations, as well as transparency and explainability, and (2) the unintended consequences of using autonomous technology-based agents in terms of de-evolution of social interaction, prioritization of machine teammates, psychological health, and biased algorithms. Originality/value Key issues related to the affective and cognitive implications of using autonomous technology-based agents, design issues, and unintended consequences highlight key contemporary research challenges that allow researchers in this area to leverage compelling questions that can guide further research in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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