Ensuring Fairness and Transparency with Algorithm Bias Auditing, Explainable AI Services, and MLOps

As a leading provider of custom AI and ML model development, we understand the transformative power of these technologies. However, with great power comes great responsibility.

In the race to achieve corporate savings through automation, algorithm developers must account for bias in their models. Even industry giants like Amazon have faced repercussions due to biased algorithms. In 2018, they had to scrap an internal recruiting tool that exhibited gender bias against women. This highlights the importance of addressing algorithm bias to ensure fair and ethical AI-driven decisions.

The inadvertent introduction of bias into algorithms poses significant risks to businesses, including litigation, regulatory challenges, and damaged reputations. We have partnered with Epstein Becker Green and EBG Advisors, combining our award-winning AI capabilities with their algorithm bias auditing and risk management expertise to offer comprehensive solutions.

Mosaic recommends Explainable AI services and MLOps for any production deployment. Read our XAI thought leadership here.

Our Explainable AI (XAI) Services & Algorithm Bias Auditing Approach

At Mosaic, we follow a rigorous algorithm bias auditing approach in alignment with the AI Risk Management Framework developed by the National Institute of Standards and Technology (NIST). By following this process, we help firms audit algorithms for bias and enhance their explainability, ensuring that AI systems are not only accurate but also fair and transparent in their decision-making processes.

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Model Evaluation and MLOps Integration

We thoroughly evaluate the algorithms that underpin the AI decision engine, drawing back on 20+ years of using math to inform decisions. This includes reviewing decisions made on the training data and metrics captured during model validation. We review your data from an objective, third-party viewpoint to ensure its quality and conduct an in-depth analysis of model inputs, outputs, data sets, training code, and the model code itself. The goal is to set a solid foundation for understanding potential biases and ensuring transparency in AI decision-making, using MLOps techniques to streamline the process.

Bias Assessment and Fairness Evaluation

To ensure compliance with ethical standards, we review existing or proposed regulations & frameworks relevant to the algorithm and use case, such as non-discrimination based on race or gender. Mosaic can tap legal expertise through a partnership with Epstein Becker Green when required. Various bias metrics and fairness evaluation techniques are employed to quantify and evaluate biases across different demographic groups. Our team conducts root cause analysis to identify the sources of bias, allowing for a deeper understanding of why biases may exist in the AI model’s outputs.

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Explainability and Model Transparency

By integrating MLOps with explainability tools, we provide insights into the model’s behavior, including feature importance analysis and local and global interpretability, while ensuring the deployment of explainable models in production and maintaining a feedback loop for model improvement. The findings and recommendations are documented comprehensively in a report, ensuring accountability and building trust in the AI system by making its decision-making process more understandable to users.

Ethical AI Partnerships & Procedures

Mosaic has joined forces with a National law firm – Epstein Becker Green – to offer comprehensive AI risk mitigation.

Working with the legal experts at EBG and EBG Advisors, Mosaic can bring to bear a comprehensive Ethical AI solution for any organization concerned with AI risk.

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Explainable AI Services One-Pager

Why Choose Mosaic Data Science for Explainable AI Services

Dedicated XAI Expertise

As AI models become more complex, their decision-making processes can appear opaque. Explainable AI (XAI) is the key to unlocking the “black box” of AI systems, providing transparency and insights into how decisions are made. Our XAI techniques foster trust, accountability, and interpretability, empowering stakeholders to understand and act confidently upon AI-driven results.

Holistic Approach to Data

Our holistic approach differentiates us from others because we focus on developing custom applications that fit your ecosystem rather than trying to fit prebuilt solutions that don’t integrate into your workflow.  This involves focusing on how the model will ultimately provide value to the business as much or sometimes more than focusing on building the most accurate model.

Proven Track Record

Since 2014, we’ve built a long track record of successful partnerships with organizations across every major industry. Our unwavering focus on data science ensures that we stay at the forefront of industry trends, offering you cutting-edge solutions that deliver meaningful results.

Access to Top Talent

With Mosaic, you have flexible, on-demand access to a team of high-performing data scientists. Whether you need support for a short-term project or ongoing assistance, our experts are here to help you navigate the complexities of data and AI with ease.

Ready to ensure fairness, transparency, and compliance in your AI-driven processes?

Contact us today to learn more about our algorithm bias auditing and explainable AI services. Together, let’s unlock the full potential of your data science endeavors and build a more responsible and inclusive AI future for your organization.