SUCCESS STORIES / IT Agility

Responsible AI fosters transparency and trust

Our Responsible AI solution with explainability, fairness, and model performance helps minimize bias and promote end-user trust.

Background

Artificial intelligence has become an indispensable catalyst in business today. The rise of AI is creating new opportunities to improve the lives of people around the world.

One-third of companies leverage AI in many use cases to improve their existing processes, pave the way for new revenue streams, meet regulatory needs across geographies, and earn their customer's faith. However, the lack of trust, transparency and secure decision-making in AI systems prevented them from realizing their true potential.

How can AI be used responsibly in business, boosting transparency and accountability?

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30% Savings in OpEx

4X Faster decision-making

Lower model bias, risks, and costs of model governance

Client Situation

Our client is a leading communications provider in the Netherlands. When responsible AI became a mandate, the client took multiple initiatives to actively pursue the advancement in AI using a collaborative ecosystem approach.

The client was spending a lot of time developing a comprehensive framework to document the organization's approach to tackling ethical and legal challenges associated with Artificial Intelligence (AI). However, the client was at the project’s nascent stage. They lacked the necessary expertise to fully realize their potential.

They chose Prodapt to bring standardized systems and governance that could facilitate the evolution of AI with appropriate definitions of design principles and methods across the company.

Our approach to developing technologies is backed by the responsible usage of Artificial Intelligence (AI).

Diagnosis

The implementation of AI lacked mechanisms to arrive at fair and interpretable predictions due to its opaque nature/Black-box approach, thus hindering its ability to provide unbiased results.

There were inadequate techniques to comprehend complex and opaque ML models and interpretable predictions. Analyzing model behaviors using insights was falling short of fairness, explainability, and model quality, hindering their efforts to scale AI.

Furthermore, the client needed guidance in recognizing and quantifying the ethical application and algorithmic impact of data, models, and their respective outcomes. So, continuous monitoring became critical to combat operational challenges due to data drift, data integrity, model decay, and potential bias.

Solving It

We used three guiding principles of responsible AI: explainability, model interpretability, and fairness. These principles were instrumental in enabling the client’s business and data science teams to interpret and understand AI decisions. As a result, we were able to proceed with the use of AI efficiently and ethically.

Our explainability mechanisms, Local Interpretable Model-agnostic Explanation (LIME), and the SHaplev Additive explainable model (SHAP) enabled a complete view of their decisions powered by AI to improve transparency and trust in decision-making.

We built a novel framework that brings in ‘Interpretability’ and ‘Fairness’ to formulate principles that use interpretability to enhance fair AI’s acceptability.

We built responsible AI with a model performance management system, which continuously evaluates model performance, compares model predictions, quantifies model risk, and optimizes performance.

We implemented an AI-responsible dashboard and model monitoring system to ensure all metrics are managed at scale. This system can generate feature attributes for model predictions and enables teams to investigate model behaviors through interactive graphs.

The successful implementation of responsible AI has facilitated 4X faster and more trustworthy decision-making so far. In addition, it will help the client save 30% of OpEx, reduce the impact of model bias, and lower the risk and cost of model governance.

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