Enable AI Explainability, Auditability and Trust through Cloud Platforms

Equip your organization with explainable and auditable AI you can trust by leveraging the robust XAI capabilities of leading cloud service providers.

Artificial intelligence (AI) is rapidly transforming businesses and industries across the globe, from optimizing supply chains to providing hyper-personalized ecommerce recommendations.

However, even as AI adoption accelerates, a key question looms: How much do you really trust your AI models?

Many AI models act as black boxes, performing predictions without any visibility into the underlying logic driving such model inferences.

Lack of AI explainability can undermine stakeholder trust, hide unfair bias and limit proper AI governance and auditing.

As use of AI becomes more widespread, concerns about potential bias and AI discrimination are rising.

One way to mitigate such valid concerns is to leverage explainable AI (XAI).

Explainable AI enables humans to understand how AI models make decisions.

Such understanding of how AI models work can identify and reduce potential AI bias while building more trustworthy AI systems.

The Growing Importance of Explainable AI

There are several compelling reasons why explainable AI is becoming critically important:

• Enhancing Trust in AI: When people understand how AI models work, they are far more likely to trust AI decisions. AI model explainability enables transparency into the logic of AI models.
• Identifying Model Bias: AI models can reflect biased data. Explainable AI helps identify such biases so you can responsibly address them.
• Boosting Model Performance: By gaining deeper transparency into the drivers of AI model inference, data scientists and AI engineers can fine-tune models for higher accuracy and make the models more effective drivers of achieving business goals.
• Model Auditing and Governance: For regulated industries, such as Financial Services, AI model explainability can enable detailed model reviews, validation and documentation for auditing and compliance purposes.

How to Implement Explainable AI

There are a variety of techniques that enable explainable AI, such as:

• Feature Importance identifies the most influential features driving AI model predictions.
• Counterfactual Analysis shows how tweaking inputs would change AI model outputs.?
• Shapley Values assign a "score" to each feature representing its contribution to AI model inferences.

Visualizations and reports turn such potential complex techniques into intuitive model explanations.

Each of the leading cloud service providers has powerful explainable AI capabilities that enable deeper visibility into the drivers of AI model inferences.

Let's explore the XAI capabilities of the major cloud providers.

Explainable AI Capabilities of the Cloud Leaders

• Amazon SageMaker Clarify: Generates visual explanations of model predictions and feature importance of models built in Amazon SageMaker. Detects bias by analyzing different subgroups. Monitors models over time.
• Google Cloud Vertex Explainable AI: Explains predictions across AutoML, BigQuery ML and Vertex AI. The interactive What-If Tool probes models by tweaking inputs and groups.
• Azure Machine Learning Interpretability Toolkit: Produces model explanations and Shapley values for Azure ML models. Enables bias detection and debugging.

The above cloud solutions make explainable AI accessible to all organizations, not just to AI experts.

Monitoring Models for Fairness

Cloud-powered explainable AI enables you to continuously monitor models for unintended bias or discrimination.

For example, Google's What-If Tool enables you to interactively check AI models for fairness by modifying inputs. You can then clearly see if certain groups are experiencing skewed or unfair results.

AI explainability enables deeper transparency into model behavior. By proactively detecting and addressing AI bias, organizations can enhance ethical AI and model compliance.

Enabling Responsible AI Adoption

The future belongs to companies that implement AI responsibly, fairly and ethically.

With cloud-powered AI explainability, bias monitoring and model auditing, you can:

• Enable model transparency to build trust
• Detect AI bias to ensure the fair treatment of individuals
• Leverage detailed model reviews to implement model governance
• Confidently scale AI across the organization

Explainable, fair and auditable AI models help unlock AI's full potential while mitigating AI risks.

Interested in learning more about Explainable AI?

Let's explore how explainable AI can work for your organization.

This article originally appeared on WSaaS.