In 2023, significant strides have been made in establishing laws and standards for responsible AI. Internationally, the EU AI Act is advancing in negotiations, with expectations of enforcement by 2026. In the US, NIST released a voluntary AI Risk Management Framework to aid organizations in addressing AI risks, and oversight bodies like the EEOC, FTC, and SEC have increased their focus on AI. The ISO and IEC are also collaboratively developing AI standards, including the AI Management System (ISO/IEC 42001 – AIMS), outlining responsible AI system management requirements.
While challenges may arise in navigating this rapidly evolving landscape, there are proactive steps organizations can take right away. Here are five key actions your organization can take immediately:
Conduct an AI Maturity Assessment: Evaluate your organizational policies, processes, and procedures against industry best practices like ISO/IEC 42001 and NIST’s AI Risk Management Framework. This assessment should examine the pillars of people, culture, and processes. Using these learnings, develop a roadmap to address the identified gaps in a prioritized manner.
Enhance Enterprise AI Governance Mechanisms: Integrate ethical decision-making and AI governance practices within your organization’s operating procedures. Focus on enhancing existing data, cybersecurity, and privacy mechanisms rather than building new ones to reduce overburdening your data & analytics practitioners and to allow innovation to flourish.
Assess the Risk and Impact of AI Systems: Consider implementing a way to assess the risk and potential consequences of your AI systems before they go into deployment. Examine bias, transparency, safety, security, and societal impacts and involve diverse stakeholders like legal, compliance, and risk management teams to identify and mitigate risks, improving responsible AI system design in an innovation-friendly manner.
Validate Technical Robustness of AI Systems: Thoroughly evaluate your AI systems to ensure optimal performance across diverse scenarios. Scrutinize the quality and comprehensiveness of the data used to train these models. Examine AI systems for any biases and ensure they consistently achieve high accuracy. Consider collaborating with external experts specializing in model validation and assessments to enhance this process.
Operationalize a Stakeholder Engagement Strategy: Develop a stakeholder engagement strategy involving diverse stakeholders across the AI lifecycle. Define roles and responsibilities for legal, compliance, analytics, privacy, technology, and development teams. Support these stakeholders with the necessary change management and education materials to ensure long-term adoption and adherence to newly introduced AI governance mechanisms.
Not sure where to get started? INQ’s portfolio of AI services is customized to fit your specific needs and get you AI-ready. To learn more, visit our website at www.inq.consulting or contact us at email@example.com.