User Driven Model Adjustment via Boolean Rule Explanations
Authors: Elizabeth M. Daly, Massimiliano Mattetti, Öznur Alkan, Rahul Nair5896-5904
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation |
| Researcher Affiliation | Industry | Elizabeth M. Daly, Massimiliano Mattetti, Oznur Alkan, Rahul Nair IBM Research Dublin, Ireland |
| Pseudocode | Yes | Algorithm 1: Generate Response, Algorithm 2: Evaluate Feedback Rules, Algorithm 3: Example of a transformation on a categorical feature when the class label is preserved, Algorithm 4: Example of a transformation on a numeric feature when the class label is changed |
| Open Source Code | No | The paper uses an open-source library AIX360 (footnote 3: https://github.com/Trusted-AI/AIX360) for BRCG implementation, but does not provide access to the code for their own interactive overlay solution. |
| Open Datasets | Yes | We select four well known binary classification benchmarks from the UCI repository TIC-TAC-TOE, BANKNOTE, BANK-MKT and BREAST CANCER. https://archive.ics.uci.edu/ml/datasets/ |
| Dataset Splits | No | The paper states: 'The data is divided into 80% for training and 20% for the holdout test set.' It describes training and test splits but does not explicitly mention a separate validation set split. |
| Hardware Specification | Yes | We implement all algorithms in Python using scikitlearn (Pedregosa et al. 2011) and perform the experiments on a cluster of Intel Xeon CPU E5-2683 processors at 2.00GHz with 8 Cores and 64GB of RAM. |
| Software Dependencies | No | The paper mentions using scikit-learn and the AIX360 library for BRCG implementation, but does not specify their version numbers or the Python version used. |
| Experiment Setup | Yes | For the purposes of these experiments the underlying machine learning algorithm used is a logistic regression with 500 iteration limit1. Numeric features are pre-processed with Standard Scaler and the categorical one with a One Hot Encoder. At each iteration a batch of 10 instances is selected from the pool, labelled by the oracle with the ground truth, and then used for retraining the ML model. |