Learning When to Advise Human Decision Makers
Authors: Gali Noti, Yiling Chen
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of a large-scale experiment show that our advising approach manages to provide advice at times of need and to significantly improve human decision making compared to fixed, non-interactive, advising approaches. |
| Researcher Affiliation | Academia | Gali Noti1,2 , Yiling Chen1 1Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University 2The School of Computer Science and Engineering, The Hebrew University of Jerusalem {galinoti, yiling}@seas.harvard.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found in the paper. |
| Open Source Code | No | The data are available on the authors websites. |
| Open Datasets | Yes | As the risk-assessment component of the algorithmic assistant, we use the model of [Green and Chen, 2019b] that was trained on 47,141 defendant cases from a dataset collected by the U.S. Department of Justice [DOJ., 2014] (top diagram in Figure 1a)... For learning the advising policy, we train a randomforest model on experimental data of human predictions from [Green and Chen, 2019a]. |
| Dataset Splits | No | For learning the advising policy, we train a randomforest model on experimental data of human predictions from [Green and Chen, 2019a]... The training data consist of 6,250 predictions made by 250 human participants, for 500 defendant cases. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | No | The paper describes the experimental treatments and participant numbers but does not provide specific experimental setup details like hyperparameter values, model initialization, or specific optimizer settings. |