Lifecycle and Key Dimensions of an AI System. Modified from OECD (2022) OECD Framework for the Classification of AI systems — OECD Digital Economy Papers.

• Implement strong data privacy regulations by creating and enforcing robust data privacy policies that protect personal data and uphold individuals' rights to control how their data is used.
• Ensure the organization's AI initiatives are agile and adhere to relevant laws and standards, including the proposed European Union Artificial Intelligence Act (The AI Act) and the NIST AI Risk Management Framework.
• Develop transparent and accountable AI systems by working with MLOps teams to ensure model training and decision-making is transparent and auditable.
• Conduct a data mapping exercise for all AI systems, to get a clear picture of where data sits and where it moves throughout your organization
• Address AI privacy and ethical risks by proactively identifying and mitigating potential concerns, such as misuse of personal data, biased algorithms, and discrimination based on ethical principles.
• Collaborate with internal and external stakeholders to develop guidelines that address reducing bias, mitigating risks, and ensuring transparency, fairness, privacy, and accountability in AI systems.
• Determine criteria for classifying different levels of risk posed by AI activities, such as higher risk for facial recognition compared to spam filters.

3. Foster a privacy-focused culture around AI decision-making

As a privacy professional, you can help create a culture of privacy awareness around AI decision-making within the organization. To do this:

• Encourage open communication and dialogue between privacy, data/MLOps, product, security, and ethics teams.
• Provide regular updates on AI initiatives, highlighting changes to ethical and privacy concerns, changes in AI technologies, and areas for improvement.
• Offer guidance and support to other departments in implementing privacy-focused AI solutions and addressing potential privacy risks associated with AI projects.

4. Navigate the risks of AI

While AI offers immense potential for businesses, it also presents risks that vary in magnitude, allowing organizations to tailor their response according to the level of risk associated with each AI project. Proactively address the risks associated with AI by:

• Assessing the level of risk associated with each AI project, factoring in data sensitivity and potential impact on individuals including bias and discrimination.
• Collaborating with IT and security teams to implement stringent data protection policies and procedures based on risk assessments.
• Monitoring and ensuring compliance with relevant data protection laws and regulations and advising on best practices for data anonymization and pseudonymization in AI applications.
• Establishing clear and transparent consent mechanisms for data collection and usage in AI systems and providing opt-out mechanisms.
• Informing the organization about evolving AI regulations and standards, incorporating AI risk management into the organization's risk management frameworks, and focusing on unique aspects of AI-related risks, such as the potential for autonomous decision-making and its implications on liability and regulatory compliance.

Data source: European Commission

Data mapping to AI Governance to responsible AI

To get to the promised land of responsible AI use across your organization, an AI governance framework is a necessity. In other words, applying AI governance best practices to your current state AI systems and tools can get you to the desired future state of responsible AI. You can get started by answering the following questions:

1. What are the current AI systems that my organization utilizes?
2. What are the sources of data that power these systems? What are respective collection mechanisms here?
3. How is this data processed after collection? For what purpose is it processed?
4. After this data passes through your AI systems, what is the outcome? 
   1. What decisions are taken based on these outcomes? 
   2. Where does the data sit after passing through these systems? 
   3. Are there any retention requirements that apply?

If this sounds a lot like a data mapping exercise to you – that’s because it is. The most comprehensive step towards understanding how your organization can achieve responsible AI is with a thorough data map that lays out exactly how and where the data you collect moves throughout your organization. After completing this step, it provides the basis to then determine the appropriate data policies and build out the governance framework that needs to be applied to your AI systems. 

Empowering privacy teams to support responsible AI

As AI continues to transform the business landscape, privacy professionals have a pivotal role in ensuring responsible AI adoption within organizations. By educating board members and CEOs on responsible AI and data privacy, developing a comprehensive AI strategy and responsible AI framework, fostering a privacy-focused culture around AI decision-making, and navigating the risks of AI, privacy professionals can help organizations effectively harness the power of AI while safeguarding user privacy and maintaining trust. With cross-collaboration and a focus on privacy, organizations can build holistic trust programs that prioritize responsible and ethical AI innovation.

As society redefines risk and opportunity, OneTrust empowers tomorrow's leaders to succeed through trust and impact with the Trust Intelligence Platform.

To learn more about how the market-defining Trust Intelligence Platform from OneTrust can help you build trust into the center of your operations and culture, click here.