May 16, 2023

Looted art: I asked ChatGPT for assistance writing code in R to classify 20,000 artworks

I asked ChatGPT for help writing a program in R to classify whether an artwork is ok, a forgery, or Nazi loot. What do you think of the result?



PROMPT

Given a dataset with 20,000 provenance texts known to have provenance gaps for the Nazi era, please indicate the steps to calculate the probability for each of the 20,000 artworks that it is 1) neither a forgery nor looted art; 2) a forgery; 3) looted art.


CHATGPT

While an AI cannot directly compute these probabilities, I can provide a general outline for how a researcher might approach this problem.

  1. Data Preprocessing: Clean and standardize the provenance texts so that they can be analyzed consistently. This could involve tasks like removing extraneous characters, standardizing date formats, or separating different sections of the provenance records.

  2. Feature Extraction: Identify key features from the provenance texts that might be associated with forgeries or looted art. These features could include things like the length of the provenance gap, the presence of speculative language, or the mention of specific people or locations known to be associated with art forgery or looting.

  3. Label the Dataset: Ideally, you would have a subset of your 20,000 artworks for which the status (forgery, looted, or neither) is already known. You could use this subset to train a machine learning model. If such a labeled subset does not exist, it might be necessary to label a portion of the artworks manually, or use some form of unsupervised learning or anomaly detection.

  4. Train a Model: Use the labeled subset of your data to train a machine learning model that can predict the status of an artwork based on the features extracted from its provenance text. This could involve techniques like logistic regression, decision trees, or neural networks, depending on the complexity of the task and the amount and quality of your data.

  5. Test and Validate the Model: After training the model, test its performance on a separate subset of your data that was not used in training. This will give you an idea of how well your model generalizes to new data.

  6. Predict the Status of Artworks: Once you are satisfied with the performance of your model, use it to predict the status of the remaining artworks in your dataset. The output would be a probability for each artwork that it is a forgery, looted, or neither.

  7. Interpret and Evaluate the Results: Analyze the results, taking into account the limitations and uncertainties of your model. Remember that these predictions are probabilistic and not definitive judgments.

It's important to note that this is a complex and sensitive task, with many potential pitfalls. The accuracy of your results will depend heavily on the quality and representativeness of your data, the appropriateness of your chosen features and model, and the rigor of your validation process.

PROMPT

Please write code in R to do this, assuming that steps 1, 2 and 3 have already been completed

CHATGPT


Sure, I can provide you with a rough skeleton for the latter steps using R. For the purpose of this example, let's assume that your data is in a data frame called artwork_data and you have two features, feature1 and feature2, and the label status. The status can be 'neither', 'forgery', or 'looted'.

I will use the randomForest package for this example:


# install the necessary package if you haven't already install.packages("randomForest") # load the package library(randomForest) # split the data into training and testing sets set.seed(123) # for reproducibility train_indices <- sample(1:nrow(artwork_data), nrow(artwork_data) * 0.7) train_data <- artwork_data[train_indices, ] test_data <- artwork_data[-train_indices, ] # train the model model <- randomForest(status ~ feature1 + feature2, data = train_data, ntree = 100) # print summary of the model print(model) # use the model to make predictions on the test set predictions <- predict(model, newdata = test_data) # evaluate the model confusion_matrix <- table(test_data$status, predictions) print(confusion_matrix) # calculate accuracy accuracy <- sum(diagonal(confusion_matrix)) / sum(confusion_matrix) print(paste("Accuracy: ", round(accuracy, 2)))


Please note that this is a very simplified example. In a real-world scenario, you would likely have more features, and you would need to do more extensive data preprocessing and model tuning. For instance, you might need to normalize your features, deal with missing values, or try different parameters for your model. You might also want to try different models to see which one performs best.

Also, remember that any model's predictions are only as good as the data it's trained on. If your data is not representative of the broader population of artworks, or if it does not contain sufficient information to distinguish between forgeries, looted art, and other art, then your model's predictions may not be accurate.


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Note to readers

Question to coders, machine learning, NLP and AI people: Where is the best place to publish a labelled dataset with key features identified so that the programming community can use it?



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