A.I. Tools

Giskard Releases Giskard Bot on HuggingFace: A Bot that Automatically Detects Issues of the Machine Learning Models You Pushed to the HuggingFace Hub

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In a groundbreaking development published on November 8, 2023, the Giskard Bot has emerged as a game-changer in machine learning (ML) models, catering to large language models (LLMs) and tabular models. This open-source testing framework, dedicated to ensuring the integrity of models, brings a wealth of functionalities to the table, all seamlessly integrated with the HuggingFace (HF) platform.

Giskard‘s primary objectives are clear:

Identify vulnerabilities.

Generate domain-specific tests.

Automate test suite execution within Continuous Integration/Continuous Deployment (CI/CD) pipelines.

It operates as an open platform for AI Quality Assurance (QA), aligning with Hugging Face’s community-based philosophy.

One of the most significant integrations introduced is the Giskard bot on the HF hub. This bot allows Hugging Face users to publish vulnerability reports automatically whenever a new model is pushed to the HF hub. These reports, displayed in HF discussions and the model card via a pull request, provide an immediate overview of potential issues, such as biases, ethical concerns, and robustness.

A compelling example in the article illustrates the Giskard bot’s prowess. Suppose a sentiment analysis model using Roberta for Twitter classification is uploaded to the HF Hub. The Giskard bot swiftly identifies five potential vulnerabilities, pinpointing specific transformations in the “text” feature that significantly alter predictions. These findings underscore the importance of implementing data augmentation strategies during the training set construction, offering a deep dive into model performance.

What sets Giskard apart is its commitment to quality beyond quantity. The bot not only quantifies vulnerabilities but also offers qualitative insights. It suggests changes to the model card, highlighting biases, risks, or limitations. These suggestions are seamlessly presented as pull requests in the HF hub, streamlining the review process for model developers.

The Giskard scan is not limited to standard NLP models; it extends its capabilities to LLMs, showcasing vulnerability scans for an LLM RAG model referencing the IPCC report. The scan uncovers concerns related to hallucination, misinformation, harmfulness, sensitive information disclosure, and robustness. For instance, it automatically identifies issues such as not revealing confidential information about the methodologies used in creating the IPCC reports.

But Giskard doesn’t stop at identification; it empowers users to debug issues comprehensively. Users can access a specialized Hub on Hugging Face Spaces, gaining actionable insights on model failures. This facilitates collaboration with domain experts and the design of custom tests tailored to unique AI use cases.

Debugging tests are made efficient with Giskard. The bot allows users to understand the root causes of issues and provides automated insights during debugging. It suggests tests, explains word contributions to predictions and offers automatic actions based on insights.

Giskard is not a one-way street; it encourages feedback from domain experts through its “Invite” feature. This aggregated feedback provides a holistic view of potential model improvements, guiding developers in enhancing model accuracy and reliability.

Check out the Reference Article. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 32k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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