Troubleshooting and analysis – AI Governance

Troubleshooting and analysis

Regardless of stringent checks, data processes can falter. Here, data lineage shines by assisting teams in pinpointing the origin of errors in their systems, whether in data pipelines, applications, or models. Such pinpoint accuracy drastically trims down the debugging duration, translating to immense time and effort savings.

In summary, the systematic mapping of the journey of data, or data lineage, is not just a luxury; it’s a necessity in today’s data-rich environments, enabling clearer decision-making, enhanced compliance, and more efficient operations.

Of course, the point of gathering this data is often to train an ML model, so let’s turn our attention toward how to think about ML governance.

Navigating the intricacy and the anatomy of ML governance

ML doesn’t operate solely by using algorithms and the data they ingest. Instead, its essence lies in constructing models responsibly, a task underpinned by governance. Just as governance has been the bedrock of the realm of data, it’s equally crucial for ML, especially in aspects such as accountability, standardization, compliance, quality, and clarity. Let’s discuss this topic in greater detail in the following sections.

ML governance pillars

Unlocking ML’s potential is rooted in ensuring that models meet the following criteria:

  • Aligns with relevant regulatory and ethical benchmarks
  • Exhibits consistent outcomes and performance
  • Illuminates their development and implications in a transparent way
  • Can undergo regular quality assessments and updates
  • Adheres to standard documentation and cataloging protocols

While adherence to industry-specific regulations sets the baseline, the navigation of the broader spectrum of ethical concerns often requires a nuanced approach. The essence of governance extends beyond mere legalities, delving into the realm of what is morally right. Here, proactive assessments and a robust evaluation process come into play, ensuring models aren’t just compliant but are also ethically sound.

Model interpretability

In the realm of ML, understanding how a model arrives at its decisions is crucial. This is not just for academic interest but has significant real-world implications, especially when decisions impact human lives, such as in healthcare or criminal justice.

Consider a healthcare system where ML models predict the likelihood of patients developing certain diseases based on their medical records. A model might predict that a patient has a high risk of developing diabetes. But why did the model make this prediction? Is it due to the patient’s age, genetic history, dietary habits, or some other factor?

Local interpretable model-agnostic explanations (LIME) is a tool that was developed to shed light on this black box of ML predictions. It works by perturbing the input data slightly (adding some noise) and observing how these changes affect the model’s predictions. By doing this many times, LIME builds up a picture of which input variables are most influential regarding a given prediction.

However, as with any tool, LIME is not infallible. It provides approximations of model behavior, not exact explanations. Furthermore, its effectiveness can vary depending on the model and data at hand.

This highlights the necessity of ML governance. Ensuring that tools such as LIME are used correctly, understanding their limitations, and supplementing them with other methods when necessary are pivotal. It’s not enough for a model to be accurate; it must also be transparent and its decisions explainable, especially in high-stakes situations. ML governance policies can set standards for interpretability and guide the proper use and interpretation of tools such as LIME.

For example, the following code segment demonstrates how to leverage the LIME tool by using a sentiment analysis model sourced from the Model Hub of Hugging Face. While this script provides an interpretability layer to the model by identifying influential words/features in the input, it’s imperative to understand that such interpretations provide approximations. The highlighted words can give insights into the model’s decisions, but they might not capture the entirety of the model’s complex reasoning. Hence, while tools such as LIME are valuable, they should be employed judiciously within a broader framework of ML governance to ensure that the insights they offer are actionable and reliable:
# Import required modules
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from lime.lime_text import LimeTextExplainer
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(“cardiffnlp/twitter-roberta-base-sentiment”)
model = AutoModelForSequenceClassification.from_pretrained(“cardiffnlp/twitter-roberta-base-sentiment”)
# Define the prediction function for LIME
def predictor(texts):
inputs = tokenizer(texts, return_tensors=”pt”, truncation=True, padding=True, max_length=512)
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1).detach().numpy()
return probs
# Initialize LIME’s text explainer
explainer = LimeTextExplainer(class_names=[‘negative’, ‘neutral’, ‘positive’])