Introducing Holistic AI OSL - An Open-Source Library for Responsible AI Systems

Towards open and responsible AI

In recent years, the growing focus on responsible AI has sparked the development of various libraries aimed at addressing bias measurement and mitigation. Among these, the holisticai open-source library, developed by the Holistic AI Research and Engineering team, has gained recognition within the AI community as a valuable tool. With this Python release of the library, we've made key improements in significant improvements in scalability and optimization, making it easier to build efficient, robust, and responsible AI solutions.

This new release represents an important step towards creating accessible tools for integrating fairness, accountability, and transparency into AI systems, helping organizations, engineers, data scientists, and researchers to create and evaluate AI solutions that align with ethical and regulatory standards while maintaining performance and scalability. In this blog post, we describe the five technical risks covered in the library, the new documentation page, a case study showing the easy-to-use aspect of the library, and the policies for contributions and releases.

5 key technical risks related with AI systems

AI systems face a range of risks that span technical, social, and legal dimensions. holisticai addresses five critical technical risks, providing comprehensive metrics and mitigation strategies to ensure safer, more reliable AI deployment:

Bias

AI systems are being used in critical areas like recruitment and judicial decision-making, where fairness is especially important. It's essential to ensure that algorithms don't discriminate and that everyone is treated equally. However, sometimes models can contain hidden biases that result in unfair treatment of individuals or groups, which we refer to as bias in the algorithm.

HolisticAI implements tools to identify and reduce both group bias and individual bias in AI models. Group bias affects larger demographic groups, and we use metrics like Equal Opportunity and Equal Outcomes to assess fairness across these groups. Individual bias, on the other hand, ensures that similar individuals are treated similarly, regardless of group membership. With our comprehensive tools, we aim to ensure fairness in AI decision-making, no matter the context.

Explainability

While AI models have made great strides in prediction accuracy, they are often viewed as “black boxes,” meaning their inner workings can be difficult to understand from the outside. Explainability in machine learning is about shedding light on how these models make their decisions. It’s important for promoting transparency and building trust, as well as ensuring accountability in AI systems. By understanding how a model arrives at its predictions, we can verify its behavior, improve it where necessary, and communicate its decisions clearly to stakeholders.

At HolisticAI, we focus on making AI models more transparent. Our AI Governance platform provides tools that measure different aspects of explainability, including global and local feature importance, surrogate models, and specific metrics designed for tree-based models. These insights help make AI more understandable and reliable.

Robustness

Robustness in AI systems is the ability of a model to maintain reliable performance despite variations and challenges in input data, such as distribution shifts, feature changes, or adversarial attacks. This is critical for ensuring models function effectively in real-world scenarios, where data can differ from the training stage. Robust models demonstrate resilience and adaptability, which is especially important in critical applications. However, evaluating robustness is challenging, requiring adversarial testing to assess how well models perform under both natural data variations and adversarial manipulations, ultimately enhancing their reliability and security.

Security

Another important aspect of responsible AI systems is security. Ensuring the security of AI systems and the data they handle in today's data-driven landscape is critical to increasing trust. We need strategies to protect users and companies against privacy risks, secure individual identities through anonymization, defend against attribute inference attacks and apply data minimization principles to reduce sensitive information exposure. Strengthening these security measures is essential for the safe and reliable deployment of systems.

Efficacy

While accuracy is important, it's not the only factor that defines a successful AI model. HolisticAI ensures that models not only achieve high accuracy but also maintain robustness, security, and fairness across various conditions. Additionally, HolisticAI employs a series of trade-off approaches that allow for a deeper analysis of these different aspects, helping organizations better evaluate and balance the performance of their AI models in real-world scenarios.

New documentation for resources

Our new documentation page presents many tutorials and study cases, a complete API reference for our Python functions (an easy way to learn, debug, and find bugs in our implemented methods). We also encourage developers and researchers that want to contribute with more Responsible AI systems to make contact with us.

Getting started

On the "Getting Started" page, users can access various tutorials and guides covering library installation, a quickstart guide with a simple implementation of bias measurement, as well as tutorials on datasets, metrics, and mitigation methods for managing technical risks.

API reference

The ”API reference” page lists all the functions that users can implement with the library. Each function comes with detailed documentation to help you understand how it works. If you need to dig deeper, the source code is available for a closer look. The user can also use it to debug issues and track down any bugs.

Example Gallery

The ”Example Gallery” is a collection of real-world case studies showcasing the HolisticAI library in action. These examples aim to inspire developers and researchers to apply the tools in their own AI projects. We invite those who have used HolisticAI to contribute their work and share insights with the community. Your case study can help others tackle challenges like bias, explainability, and security, while building a shared knowledge base. Join the community by sharing your experience and advancing AI development together.

Datasets

The library offers several datasets for benchmarking new methods in responsible AI. These datasets also serve as learning tools to demonstrate how to assess responsible aspects of AI-based systems. As part of our ongoing efforts, we plan to add more datasets to enhance the evaluation of new responsible AI methods.

Visualizations

The library offers a wide range of visualization options for data exploration, metrics analysis, and method evaluation. You can create various types of plots to gain insights into your data, understand metric performance, and analyze the effectiveness of different methods. Whether you need to explore complex data patterns or present results clearly, the library's visualization tools are designed to support a comprehensive analysis.

External contributions and release policy

HolisticAI is a thriving, community-driven project that welcomes contributions from developers, researchers, and AI enthusiasts who share a passion for advancing Responsible AI. Our mission is to foster a dynamic ecosystem around the HolisticAI library, where contributors can collaborate on innovative projects, showcase their work, and help shape the future of ethical, accountable, and transparent AI.

We invite you to join us on this journey toward more responsible AI systems. Whether you’re interested in enhancing functionality, optimizing performance, or addressing emerging ethical challenges, your contributions can make a meaningful impact. Our contribution guidelines, available on our GitHub repository, provide clear instructions on how to submit codes, report issues, and propose new features.

By working together, we can build AI solutions that not only perform effectively but also align with the highest standards of fairness, security, and transparency!