This visitor put up is by Sunil Soares, founder and CEO of YDC – AI Governance. Beforehand, he based and led Data Asset, a knowledge administration agency. Sunil brings a deeply-researched perspective to AI governance—authoring 13 books which have formed how enterprises method knowledge and AI at scale.
The YDC group developed an AI Governance prototype in Atlan. We reused the prevailing working mannequin with property and added customized attributes and relations.
AI Use Circumstances
As mentioned in an earlier weblog, a digital twin could also be a digital duplicate of a specific affected person that displays the distinctive genetic make-up of the affected person or a simulated three-dimensional mannequin that reveals the traits of a affected person’s coronary heart. Digital twins could also be utilized to speed up scientific trials and cut back prices within the life sciences business. The YDC group carried out an outline of the Digital Twins for Medical Trials AI Use Case in Atlan.
AI Danger Assessments
We carried out an AI Danger Evaluation for the use case with Atlan. Digital twins have the potential to introduce bias dangers based mostly on the algorithms and the underlying knowledge units. We documented the bias threat evaluation and a mapping to the related rules in Atlan.
We additionally documented the privateness dangers in Atlan.
We documented different dimensions of AI threat together with Reliability, Accountability, Explainability and Safety in Atlan. For the sake of brevity, I’ve not included these screenshots right here.
This use case would probably be categorised as Excessive Danger based mostly on the Medical System class of Article 6 of the EU AI Act.
AI Danger Evaluation Workflows
We configured an AI Danger Evaluation workflow in Atlan to route the AI Danger Evaluation to the suitable events for approval.
The screenshot beneath reveals the AI Danger Evaluation in Accepted standing based mostly on approvals from the Operational Danger Administration Committee (ORMC) and the AI Governance Council.
Shadow AI Governance to Ingest Metadata from ServiceNow CMDB and YDC_AIGOV Brokers on Hugging Face to Spotlight COTS Apps with Embedded AI
In an earlier weblog, I mentioned Shadow AI Governance and the YDC_AIGOV brokers. As half of the present train, we ingested metadata across the Industrial-off-the-Shelf (COTS) apps into Atlan. This data contains metadata equivalent to Utility Title, Privateness Coverage URL, Knowledge Particularly Excluded from AI Coaching, Embedded AI and Choose-Out Choice.
The screenshot beneath reveals Atlan earlier than operating the combination with the YDC_AIGOV brokers. The catalog solely comprises one AI Use Case (Digital Twins for Medical trials) and one utility (Google Product Providers).
After operating the combination with Atlan API, Atlan comprises a broader checklist of functions together with Actimize Xceed together with metadata in the precise panel.
Conditional Logic with Atlan API to Auto-Create AI Use Case and AI Danger Evaluation Objects
We carried out conditional logic within the Atlan API to auto-create AI use instances just for functions with embedded AI. On this case, we created an AI use case object in Atlan for Actimize Xceed as a result of Embedded AI = “Sure.”
We additionally carried out conditional logic within the Atlan API to auto-create AI Danger Evaluation objects the place Knowledge Particularly Excluded for AI Coaching = “No.” Clearly, this logic is configurable.
This can be a fundamental AI Governance configuration in Atlan with extra to return!
This put up was initially printed on Your Knowledge Join. Learn the unique article right here.
