Get Started with TensorFlow on Law and Statistics

As a full-stack developer specializing in machine learning, I‘ve seen firsthand the transformative potential of AI in domains like law and government. The legal industry in particular is on the cusp of a data revolution, with new analytics tools promising to streamline case research, contracts review, e-discovery and more. But building robust models for these tasks requires navigating some unique challenges – from messy unstructured text to complex hierarchical label spaces to ever-shifting legal concepts.

The TensorFlow library is well-suited to tackle these challenges, thanks to its flexibility, scalability, and suite of natural language processing tools. In this expert guide, we‘ll walk through an end-to-end example of using TensorFlow to build a binary classifier for legal case outcomes. Along the way, we‘ll explore techniques for text preprocessing, model architecture, performance evaluation, and interpretation that are particularly relevant to the legal analytics domain.

Why Legal Analytics Matters

But first – why should you care about legal analytics in the first place? A few key statistics paint a picture of a field on the rise:

• The global legal analytics market is expected to reach $1.8 billion by 2022, growing at a CAGR of 32% (Markets and Markets, 2017)
• 84% of legal departments are using or plan to use AI tools for contract analysis (Gartner, 2019)
• 79% of lawyers believe AI will have a significant impact on the practice of law within 5 years (Altman Weil, 2020)

This growth is driven by the potential for data-driven tools to make legal practice more efficient, consistent, and quantitative. Some common use cases include:

• Legal research: AI systems can help surface relevant cases, statutes, and treatises based on semantic search queries or document similarity
• Contract review: Machine learning models can be trained to extract key clauses, identify risky language, and compare contracts to templates at scale
• Case outcome prediction: Classifiers and regression models can estimate the likelihood of different outcomes like damages awarded, settlement amounts, or length of sentence to inform legal strategy
• E-discovery: Clustering and concept modeling techniques can group documents by topic, find related files, and prioritize docs for review

With that context in mind, let‘s dive into a TensorFlow implementation that captures many of these ideas.

Example: Predicting Case Outcomes from Textual Features

Our example task will be predicting binary case outcomes (plaintiff win vs. defendant win) from the textual content of U.S. court opinions. We‘ll structure this as a supervised text classification problem, using a logistic regression model with bag-of-words features. While simple, this setup illustrates some core techniques that generalize to more sophisticated legal AI systems.

Load and Preprocess Data

We‘ll assume our training data is stored as a CSV with columns for the opinion text and binary outcome label. To load this into a TensorFlow Dataset:

import tensorflow as tf

def load_dataset(csv_path):
  return tf.data.experimental.make_csv_dataset(
      csv_path, 
      batch_size=32,
      label_name=‘outcome‘,
      select_columns=[‘text‘, ‘outcome‘],
      num_epochs=1)

data = load_dataset(‘opinions.csv‘)

Next we preprocess the text into a bag-of-words representation using TensorFlow‘s TextVectorization layer:

import tensorflow as tf

vectorizer = tf.keras.layers.TextVectorization(
    max_tokens=10000,
    output_mode=‘count‘,
    output_sequence_length=1000)

# Adapt preprocessor to training text
vectorizer.adapt(data.map(lambda x, y: x))

# Apply to data
data = data.map(lambda x, y: (vectorizer(x), y))

This maps each opinion to a 1000-dimensional vector of counts for the top 10k words in the corpus.

Build Model Architecture

With data in hand, we can define a simple logistic regression model using Keras:

model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation=‘relu‘),
    tf.keras.layers.Dense(1, activation=‘sigmoid‘)
])

model.compile(
    optimizer=‘adam‘,
    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
    metrics=[‘accuracy‘])

This stacks a fully-connected hidden layer with ReLU activation and an output layer with sigmoid to produce class probabilities. We use binary cross-entropy loss and the Adam optimizer.

Train and Evaluate

With model built, training is one line:

model.fit(train_data, epochs=10, validation_data=test_data)

To rigorously assess performance, we should evaluate on a held-out test set:

loss, accuracy = model.evaluate(test_data)
print(f‘Test accuracy: {accuracy:.3f}‘)

For imbalanced legal datasets, more granular metrics like precision, recall, and F1 score may be appropriate:

from tensorflow.keras.metrics import Precision, Recall

precision = Precision()
recall = Recall()

for batch in test_data:
  X, y = batch
  probs = model(X)
  precision.update_state(y, probs)
  recall.update_state(y, probs)

print(f‘Test precision: {precision.result().numpy():.3f}‘)  
print(f‘Test recall: {recall.result().numpy():.3f}‘)

Interpret Results

To understand model decisions, we can use the reduce_sum trick to extract word-level weights:

weights = model.trainable_variables[0]
vocab = vectorizer.get_vocabulary()

word_scores = dict(zip(vocab, tf.squeeze(weights))) 
sorted_scores = sorted(word_scores.items(), key=lambda x: x[1], reverse=True)

print(‘Top positive words:‘)
for word, score in sorted_scores[:10]:
    print(f‘{word}: {score:0.3f}‘)

print(‘Top negative words:‘)  
for word, score in sorted_scores[-10:]:
    print(f‘{word}: {score:0.3f}‘)

This surfaces words that push the model toward plaintiff or defendant predictions.

TensorFlow makes it easy to swap in other interpretability techniques like integrated gradients or LIME for more nuanced attribution scores.

Extensions and Challenges

This basic example illustrates the key pieces of a TensorFlow text classifier for legal analytics: data ingest, tokenization, model building, evaluation, and interpretation. But there are many directions to expand this template:

• Use pre-trained word embeddings like word2vec or BERT to capture richer semantics
• Incorporate document metadata like court, jurisdiction, judge as additional features
• Add convolutional or recurrent layers to model word order and context
• Train a multi-class or multi-label model for finer-grained issue classification
• Apply attention mechanisms to highlight key phrases and sentences
• Utilize sources beyond opinion text like docket entries, briefs, pleadings

However, legal AI developers must also grapple with some unique challenges:

• Extreme label imbalance: 95%+ of cases may have a ‘typical‘ outcome
• Changing legal concepts and language over time
• Hierarchical, inconsistent labeling taxonomies
• Data access and privacy constraints
• High stakes decisions with little margin for error

Best practices to address these issues include:

• Upsampling/downsampling techniques for imbalanced data
• Adaptive models that can learn from small amounts of new data
• Label normalization and ontology mapping
• Differential privacy and federated learning for model training
• Risk-calibrated classification thresholds and human-in-the-loop oversight

Ethical development of legal AI also requires proactive steps to audit for bias, protect individual privacy, explain decisions, and preserve human agency. The law is ultimately a human institution, so these systems must be designed to empower rather than replace human legal judgment.

Conclusions

TensorFlow provides an accessible yet powerful foundation for data scientists and developers tackling legal analytics challenges. Its flexibility, interpretability, and scalability align well with the unique characteristics of law as a data domain.

As legal AI continues its rapid growth, we can expect to see TensorFlow at the heart of many new tools for case outcome prediction, semantic search, contract analysis, and beyond. By starting with simple models like this one and iterating with domain expertise, developers can build bespoke solutions tuned to the nuances of specific legal datasets and use cases.

Mastering TensorFlow for legal analytics doesn‘t require a law degree – just a willingness to grapple with messy text, complex labels, and high-stakes decisions. Hopefully this guide has equipped you with the basic building blocks to start applying TF in your own legal AI projects. The future of law and technology is bright indeed!