Machine learning models/Production/Dutch Wikipedia goodfaith edit

Model card
This page is an on-wiki machine learning model card.
A model card is a document about a machine learning model that seeks to answer basic questions about the model.
Model Information Hub
Model creator(s)	Aaron Halfaker (User:EpochFail) and Amir Sarabadani
Model owner(s)	WMF Machine Learning Team (ml@wikimediafoundation.org)
Model interface	Ores homepage
Code	ORES Github, ORES training data, and ORES model binaries
Uses PII	No
In production?	Yes
Which projects?	Dutch Wikipedia
This model uses data about a revision to predict the likelihood that the revision is in good faith.

Motivation

Not all damaging edits are vandalism. This model is intended to differentiate between edits that are intentionally harmful (badfaith/vandalism) and edits that are not intended to be harmful (good edits/goodfaith damage). The model provides a guess at whether or not a given revision is in good faith, and provides some probabilities to serve as a measure of its confidence level. This model was inspired by research of Wikipedia's quality control system and the potential for vandalism detection models to also be used as "goodfaith newcomer" detection systems.

Users and uses

Use this model for

• This model should be used for prioritizing the review and potential reversion of vandalism on Dutch Wikipedia.
• This model should be used for detecting goodfaith contributions by editors on Dutch Wikipedia.

Don't use this model for

• This model should not be used as an ultimate arbiter of whether or not an edit ought to be considered good faith.
• The model should not be used outside of Dutch Wikipedia.

Current uses

• Dutch Wikipedia uses the model as a service for facilitating efficient edit reviews or newcomer support.
• On an individual basis, anyone can submit a properly-formatted API call to ORES for a given revision and get back the result of this model.

Example API call:

https://ores.wikimedia.org/v3/scores/nlwiki/1234/goodfaith

Ethical considerations, caveats, and recommendations

Dutch Wikipedia decided to use this model. Over time, the model has been validated through use in the community.

This model is known to give newer editors lower probability of editing in good faith.

Internal or external changes that could make this model deprecated or no longer usable are:

• Data drift means training data for the model is no longer usable.
• Doesn't meet desired performance metrics in production.
• Dutch Wikipedia community decides to not use this model anymore.

Model

Performance

Test data confusion matrix:

Label	n	~True	~False
True	17625	16948	677
False	622	112	510

Test data sample rates:

Rate	Sample	Population
sample	0.966	0.034
population	0.965	0.035

Test data performance:

Statistic	True	False
match_rate	0.934	0.066
filter_rate	0.066	0.934
recall	0.962	0.82
precision	0.993	0.439
f1	0.977	0.572
accuracy	0.957	0.957
fpr	0.18	0.038
roc_auc	0.972	0.971
pr_auc	0.999	0.709

Implementation

Model architecture

{
    "type": "GradientBoosting",
    "params": {
        "scale": true,
        "center": true,
        "labels": [
            true,
            false
        ],
        "multilabel": false,
        "population_rates": null,
        "ccp_alpha": 0.0,
        "criterion": "friedman_mse",
        "init": null,
        "learning_rate": 0.1,
        "loss": "deviance",
        "max_depth": 3,
        "max_features": "log2",
        "max_leaf_nodes": null,
        "min_impurity_decrease": 0.0,
        "min_impurity_split": null,
        "min_samples_leaf": 1,
        "min_samples_split": 2,
        "min_weight_fraction_leaf": 0.0,
        "n_estimators": 300,
        "n_iter_no_change": null,
        "presort": "deprecated",
        "random_state": null,
        "subsample": 1.0,
        "tol": 0.0001,
        "validation_fraction": 0.1,
        "verbose": 0,
        "warm_start": false
    }
}

Output schema

{
    "title": "Scikit learn-based classifier score with probability",
    "type": "object",
    "properties": {
        "prediction": {
            "description": "The most likely label predicted by the estimator",
            "type": "boolean"
        },
        "probability": {
            "description": "A mapping of probabilities onto each of the potential output labels",
            "type": "object",
            "properties": {
                "true": {
                    "type": "number"
                },
                "false": {
                    "type": "number"
                }
            }
        }
    }
}

Example input and output

Input:

https://ores.wikimedia.org/v3/scores/nlwiki/1234/goodfaith

Output:

{
    "nlwiki": {
        "models": {
            "goodfaith": {
                "version": "0.5.1"
            }
        },
        "scores": {
            "1234": {
                "goodfaith": {
                    "score": {
                        "prediction": false,
                        "probability": {
                            "false": 0.790132053222005,
                            "true": 0.20986794677799503
                        }
                    }
                }
            }
        }
    }
}

Data

Data pipeline

Tabular data about edits is collected from the Mediawiki API, preprocessed (via log-transformations, joining with public editor data, etc.), and joined with user-generated goodfaith/damaging labels.

Training data

This model was trained using hand-labeled training data that is several years old.

Test data

The statistics reported here were calculated by selecting a random partition of the training data to hold out from the training process. The model then makes a prediction on that data, which is compared to the underlying ground truth.

Licenses

• Code: MIT license
• Model: MIT license

Citation

Cite this model card as:

@misc{
  Triedman_Bazira_2023_Dutch_Wikipedia_goodfaith,
  title={ Dutch Wikipedia goodfaith model card },
  author={ Triedman, Harold and Bazira, Kevin },
  year={ 2023 },
  url={ https://meta.wikimedia.org/wiki/Machine_learning_models/Production/Dutch_Wikipedia_goodfaith_edit }
}