Enable responsible, transparent and explainable AI workflows with IBM watsonx.governance

Challenges for Responsible AI

The promises of AI are undeniable, but so are the risks. A thoughtful governance approach enables organizations to work with AI safely and responsibly. With a solid safety net in place, there is no reason to be deterred by the revolutionary aspects of AI. Set your business on a fast track!

Obstacles to Responsible AI

1. Responding to Growing and Changing AI Regulations: Noncompliance with regulations and industry standards can cost your organization both time in supporting audits and millions in fines.
2. Risk Management and Reputation: Proactive detection of bias and drift is essential to protect customer privacy, loyalty, trust, and security. Biased or unexplainable model results can lead to brand damage and customer distrust.
3. Operationalizing AI with Confidence: Manual data science tools and processes can inadvertently introduce human errors into AI algorithms and models. Lengthy model lifecycles and manual approvals can lead to drift.

Handling AI Responsibly

Responsible AI requires governance, the process of directing, monitoring, and managing your organization’s AI activities. There is a wide variety of tools available for AI governance, yet too often, models are created without the necessary clarity, monitoring, or cataloging. Without end-to-end tracking of the AI lifecycle using automated processes, scalability and transparent operations are hindered. Explainable results remain elusive.

You may have heard of “black box models,” which are a growing concern for AI stakeholders. AI models are developed and deployed, but it is not always easy to trace how and why decisions were made — even for the data scientists who created them. These challenges lead to inefficiencies manifested as scope drift, models that are delayed or never put into production, or models that exhibit inconsistent quality levels and unrecognized risks.

Given the challenges associated with operationalizing AI, it is crucial to find solutions that ensure both efficiency and transparency. In this context, IBM watsonx.governance offers a promising answer. IBM watsonx.governance is an automated toolkit that manages both generative AI and machine learning (ML) on the IBM watsonx platform. You gain comprehensive AI governance without the high costs of switching from your current data science platform.

Before a model goes into production, it is validated to assess business risks. After going live, it is continuously monitored for fairness, quality, and drift. Regulators and auditors can access documentation that provides explanations of the model’s behavior and predictions.

You can gain insights into how the model works and what processes and training it has undergone. IBM watsonx.governance spans the entire lifecycle, and your teams receive support as they design, build, deploy, monitor, and centralize facts for the explainability of AI.

With this governance toolkit, audits can become simpler. Track and document the provenance of data, the models, and their associated metadata, as well as the pipelines. The documentation includes the techniques that trained each model, the hyperparameters used, and the metrics from the testing phases. Expect increased transparency in the behavior of each model throughout its entire lifecycle, knowledge of the data that influenced its development, and the ability to identify potential risks.

Components of the IBM watsonx.governance Solution

1. Compliance:
   • Manage AI to meet upcoming safety and transparency regulations worldwide—a “nutrition label” for AI.
   • Translate external AI regulations into policies for automatic enforcement.
   • Support adherence to external AI regulations for audits and compliance.
   • Utilize factsheets for transparent model processes.
2. Risk Management:
   • Proactively detect and mitigate risks, monitoring for fairness, bias, drift, and new LLM metrics.
   • Set preset thresholds for alerts when key metrics are breached.
   • Identify, manage, and report on risk and compliance at scale
   • Provide explainable model results in support of audits or fines.
3. Lifecycle Governance:
   • Manage, monitor, and govern AI models from IBM, open-source communities, and other model providers.
   • Automate and consolidate tools and processes to drive transparent AI at scale.
   • Monitor, catalog, and govern both generative and ML models across the AI lifecycle.
   • Automate the capture of model metadata for effortless report generation.
   • Enhance communication and collaboration with stakeholders through dynamic dashboards, charts, and dimensional reporting.

Conclusion

With IBM watsonx.governance, organizations can ensure that their AI strategies are not only effective but also responsible. This solution enables businesses to tackle the challenges of AI governance while fully leveraging the benefits of artificial intelligence.

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