Widespread AI use introduces risks such as bias, safety, transparency, and security concerns. With evolving regulations like the EU AI Act, implementing a comprehensive risk management framework is essential.

A responsible AI framework operationalises guidelines, oversight, and technical safeguards to optimise outcomes. Enterprises should adopt a three-pronged approach that includes monitoring risks, enhancing defences and development, and governing control and risk management.

The three dimensions of the strategy should broadly cover the following:

Generative AI highlights the importance of strong AI governance, emphasising ethical use beyond technology.

Codified AI policies: Enterprises must establish clear AI policies covering procurement, development, deployment, grievance redress, usage, and crisis management. These accessible policies should define stakeholder obligations and adherence levels, forming the basis of AI governance.

Metrics driven and streamlined governance: Enterprises need to define metrics in privacy, explainability, and security to track responsible AI usage. Automating compliance checks accelerates low-risk approvals.

The responsible AI office: Enterprises require a dedicated team with mandates to align ethical considerations with business objectives, avoiding conflicts of interest.

Responsible by design focuses on integrating ethical principles, fairness, transparency, accountability, and regulatory compliance across the AI lifecycle, from design to deployment. Key aspects of this approach include:

Impact and risk assessment: Enterprises must leverage automated risk assessment to identify and categorise use-cases for different risks, applying additional gating criteria and design consideration during development stage.

Red teaming: This is central to an AI testing strategy. Enterprises should have teams skilled in adversarial testing processes to expose hidden vulnerabilities in models and use cases.

Responsible AI toolkit: Enterprises need specialised tools with APIs for developers to automate and streamline responsible design and development, integrating considerations like fairness, explainability, and privacy across stages.

Legal guardrails: Organisations need to apply legal safeguards, such as contractual clauses, to minimise risks from third parties, review procurement and usage policies legally.

Aligning Gen AI with ethical standards is challenging due to the complex AI supply chain and industry-specific risks. To mitigate this, real-time guardrails that monitor AI inputs and outputs for threats are effective. Investing in responsible AI is crucial as consumer demands for AI safety grow.

The writer is Executive Vice President, Global Services Head, AI and Industry Verticals, Infosys