The AI landscape has quickly evolved into a reality where many enterprises are racing to embed AI into how they operate. AI can transform businesses but a lack of guardrails can lead to lead to ethical, legal, and regulatory violations. Roadblocks to responsible AI include the inability to:
Respond to the growing and changing AI regulations Noncompliance with regulations and industry standards can cost your organization both time in supporting audits and millions in fines. Currently there are the EU AI Act, a White House executive order on the safe, secure and trustworthy development and use of AI, and the Singapore AI Governance Framework.
Manage risk and reputation
The proactive detection of bias and drift is necessary in protecting customer privacy, loyalty, trust and security. Biased or unexplainable model results can result in brand damage and customer mistrust, internal audits and fines. Visibility into model facts is important when defending analytic decisions to management, stockholders and outside auditors.
Operationalize 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/model validation can lead to drift, and making incorrect assumptions can result in bias around age, sex, race, gender, etc.
Responsible AI requires governance, the process of directing, monitoring and managing the AI activities of your organization. IBM® watsonx.governance™ is a one-stop automated toolkit built to govern both generative AI and machine learning (ML) models on the IBM® watsonx™ platform.
Components of the watsonx.governance solution include:
Compliance—manage AI to meet upcoming safety and transparency regulations and policies worldwide—a “nutrition label” for AI
– Translate external AI regulations into policies for automatic enforcement
– Help adhere to external AI regulations for audit and compliance
– Use factsheets for transparent model processes
Risk management—proactively detect and mitigate risks, monitoring for fairness, bias, drift and for new LLM metrics
– 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
Lifecycle governance—manage, monitor and govern AI models from IBM, open-source communities and other model providers
– Automate, consolidate tools and process 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
– Increase stakeholder communication and collaboration with dynamic dashboards and, charts and dimensional reporting
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