A brand-new, comprehensive solution based on IBM Cloud Pak for Data is IBM AI Governance.
When implementing artificial intelligence (AI), many businesses experience difficulty. The proper data access, risky manual processes that limit scalability, many unsupported tools used in developing, deploying, and monitoring models, as well as platforms and practises that are not AI-optimized, are some of the challenges. These difficulties prevent organisations from delivering the transparent, explicable, and trusted AI judgements required to satisfy the rising ethical and regulatory requirements for AI today. To address these issues, AI governance is needed that is built to operationalize AI, control risk, and provide scalability while adhering to expanding AI rules.
Even in these uncertain economic times, AI governance may boost trust by promoting control and predictability to assist meet ethical and regulatory requirements for AI. Concerns from stakeholders, organisations, and clients are addressed by automating the tracking and documenting of the source of data, models, and associated metadata and pipelines. This information is useful for audits. The information should be documented and contain the data that influenced the model’s development, the methods used to train each one, the hyperparameters employed, and the metrics from testing phases. Increased transparency into the model’s behaviour throughout its lifecycle and any potential risks is the result of this documentation.
Enhancing corporate capacities, The new, all-inclusive IBM AI Governance solution was created utilising automated software and the IBM Cloud Pak® for Data, which integrates with the data science platform currently used by your company. Everything required to create a transparent, standardised model management process, including model development time, metadata, post-deployment model monitoring, and customised workflows, is part of this solution.
This new approach automates the collection of model metadata throughout the AI/ML lifecycle, freeing up data science teams to work on projects other than model documentation. Having a constant, accurate view of their models is advantageous for data science directors and model validators. Scalability and the capacity to produce visible, comprehensible results free of detrimental bias and drift are advantageous to businesses. By detecting how AI is applied and areas that require retraining, IBM AI Governance improves forecast accuracy.
To identify, manage, monitor, and report on risk and compliance initiatives at scale, IBM AI Governance uses model risk management. A strong set of workflows, improved collaboration, and increased AI regulatory compliance are all made possible by the clear, simple findings that dynamic dashboards deliver.