How (not) to Evaluate Computational Empathy: Testing the Assumptions of the Evaluation Methods in a Use-Case
Bibliographic record
Abstract
An increasing amount of successful contributions were made in the past two decades to model empathic behavior in artificial agents to enhance the interaction between humans and artificial agents in variety of contexts. However, this relatively novel research area still did not develop standard methods for evaluation. In this paper, we use an embodied conversational agent (ECA) with real time multi-modal interaction capabilities, to test the assumptions of the proposed evaluation method of computational empathy in a patient intake scenario of counselling services in university environments. Our findings show that our system was able to show more empathy compared to its human counterpart and that the proposed evaluation method was supported by the user study in determining the changes in users’ perception in empathic behavior by relying on feature-level metrics and other factors that are not directly related to empathy, which are shown to have a significant influence on the perception of empathic behavior. However, our detailed analyses showed poor reliability of the empathy questionnaire used for the system evaluation, suggesting the need for a metric that is tailored for artificial agents.
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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 | 0.071 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".