As AI becomes ubiquitous in society, alarm bells are sounding about the ethical and social implications of how the technology is used.
Incidents of AI gone wrong have had legal, reputational and financial consequences – and prompted governments to draft new AI regulations. This has highlighted the need for Responsible AI approaches to ensure that ML models are developed and used in an ethical manner to be fair, transparent and legally compliant.
Steps to enabling Responsible AI principles
Companies looking to enable Responsible AI focus on incorporating the principles of fairness, accountability, transparency and safety into their approach to AI/ML.
On the technology side, companies should ensure that their tooling enables standardized, well-documented processes with essential governance checks. Tools must guard against bias, automatically monitor model performance in production, and support explainability and auditability.
Verta helps companies mitigate risks by enabling Responsible AI principles
Use checklists and automations to standardize deployments and promotions.
Standardize model documentation, model schema and API management.
Scan models for vulnerabilities and review training data for bias.
Monitor model I/O and performance, get alerts for degradation or drift and quickly identify root causes for fast recovery.
Obtain full model reproducibility using code, data, configuration and environment variables.
Access detailed audit logs.
Contact Verta today to learn how we enable adherence to Responsible AI principles.
