The democratization and consumerization of AI is transforming industries, improving efficiency, customer experience and enhancing decision-making. However, as adoption for enterprises increases across their core operations, the need for responsible implementation has become paramount. Responsible AI ensures that AI systems are trustworthy, ethical, and aligned with societal values. Achieving this, requires a unique blend of technical, ethical, and governance-related skills.
Privacy and Security
Ensuring the security of AI systems is a specialized skillset that requires expertise in adversarial testing and red teaming, understanding different defenses specific to diverse attacks and implementing monitoring mechanisms. It involves knowledge on how to harden the models to be more resilient.
From a privacy standpoint, professionals should be familiar with privacy frameworks and techniques like data encryption, differential privacy, Secure Multi-Party Computation, Federated Learning and access controls to safeguard sensitive and personally identifiable information.
AI Governance, Legal and Ethical Skillsets
AI governance is the backbone of responsible AI. These are more strategic skills that focus on long term ethical AI alignment. It encompasses the frameworks, policies, and processes that guide how AI systems are designed, deployed, and monitored. It also includes data governance – how data is managed, stored and tracked ethically.
- Legal Compliance: Deep understanding of regulations such as the EU AI Act, GDPR, CCPA, IP protection laws etc and other industry specific laws is vital. Knowledge of risk management and governance frameworks from NIST/OECD is also essential.
- Policy Development: Crafting organizational policies for ethical AI usage and ensuring alignment with industry standards like ISO/IEC ie ISO 42001:2023 frameworks for AI management.
- Risk Management and Audits: Skills to identify, assess, and mitigate risks associated with AI systems, including ethical and operational risks, are vital along with abilities to perform both system level and process level audits.
- AI Ethics: Professionals should also possess the analytical skills to evaluate AI systems’ potential social impacts and risks, particularly on marginalized communities and recommend mitigation approaches.
Designing Process Guardrails
It involves the ability to imbibe Responsible AI by design in AI development i.e. embedding responsible AI dimensions across the AI lifecycle, from preparing the data ethically, to training and finetuning the model as per ethical guidelines to finally deploying it in production. In each phase, skills involve – understanding and implementing protocols and best practices and leveraging the right tools in the right way. It also involves skills for creating and maintaining documentation for model development and deployment, establishing audit trails and user guides etc.Technical Skillsets for building guardrails
Technical guardrails involve implementing solutions to scan and filter inputs and outputs of an AI system for threats. These systems need to be intelligent enough to recognize a wide variety of threats like prompt injections, jailbreaks, hallucinations, drift, malicious content etc. Responsible AI practitioners need to be comfortable with the latest research in this area and should be able to develop these solutions. It is vital to have skills to build interpretable models and techniques that allow stakeholders to understand how AI systems make decisions. Similarly, skills are needed for implementing algorithms and model architectures specifically designed to reduce bias, optimize model performance from a sustainability point of view, and leverage techniques like chain/graph of thoughts that help augment the model’s reasoning capabilities and reduces risks in critical applications.Responsible AI requires collaboration among diverse teams, including data scientists, ethicists, legal experts, and business leaders. Clear communication and ability to collaborate and manage alliances with organizations is crucial for simplifying technical concepts, fostering interdisciplinary contributions, and engaging effectively with stakeholders such as users, policymakers, and community representatives. As AI evolves, professionals must commit to continuous learning, research and adaptation to address emerging challenges and uphold the principles of responsible AI.
The author is Balakrishna D. R. (Bali), Executive Vice President, Global Services Head, AI and Industry Verticals of Infosys.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
- Published On Feb 12, 2025 at 09:00 AM IST
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