AI
Governance Protocols
for Organizations
When thinking about establishing AI Governance policies in an organization, the Artificial Intelligence Governance and Auditing (AIGA) program is a key source of information that can be utilised to ensure development and deployment of AI systems is conducted in an responsible, ethical and compliant manner.
The program is a set of standards and processes for best practice AI governance and auditing. This includes the following key areas that organizations can use as a guide when implementing AI Governance. The AI Summit Cape Town caught up with Sharon (Shaghayegh) Shahrokhi Terani, Product Manager, Machine Intelligence Rentention, CBC ahead of the summit to get her thoughts on why businesses should be aware of the AIGA program and the implications or impact if they don't take these standards into consideration when esatblishing AI policies in business.
Ethical Guidelines and Frameworks
Establish ethical principles: Define clear ethical principles that guide AI development and deployment. This includes fairness, transparency, accountability, and respect for privacy.
Bias mitigation: Implement strategies to detect and mitigate biases in AI models to ensure fair and unbiased outcomes.
Ethical guidelines help prevent harm to individuals and society, fostering trust in AI systems. Addressing bias ensures that AI does not perpetuate or amplify existing inequalities.
Ethical Guidelines and Frameworks
Transparency and Explainability
Model transparency: Ensure that AI models and their decision-making processes are transparent and understandable to stakeholders.
Explainability tools: Use tools and techniques that allow explanations of how AI systems arrive at specific decisions or predictions.
Transparency and explainability are crucial for accountability and trust. They enable stakeholders to understand, trust, and effectively oversee AI systems.
Data and Governance
Data quality and integrity: Implement policies to ensure the quality, accuracy, and integrity of data used in AI systems.
Data privacy and protection: Establish strong data privacy policies and practices to protect individuals' personal information.
Importance: High-quality data is essential for accurate AI outcomes. Protecting data privacy is crucial for complying with regulations and maintaining public trust.
Risk Management & Impact Assessment
Risk assessment: Conduct thorough risk assessments to identify potential risks associated with AI deployment.
Impact assessment: Evaluate the social, economic, and environmental impacts of AI systems before and after deployment. Importance: Risk and impact assessments help prevent unintended consequences and ensure that AI systems contribute positively to society.
Regulatory Compliance
Compliance with laws and regulations: Ensure that AI systems comply with relevant laws, regulations, and industry standards.
Monitoring regulatory changes: Stay updated on changes in AI-related regulations and adapt governance protocols accordingly.
Importance: Compliance with regulations helps avoid legal penalties and ensures that AI systems operate within acceptable boundaries.
Accountability and Oversight
Clear accountability structures: Define who is responsible for the outcomes of AI systems at various stages of their lifecycle.
Independent oversight: Establish independent oversight mechanisms to review and audit AI systems and their use.
Importance: Accountability and oversight ensure that AI systems are used responsibly and that there are mechanisms for addressing issues when they arise.
Stakeholder Engagement
Inclusive engagement: Engage with a diverse group of stakeholders, including employees, customers, and external experts, to gather input and feedback on AI systems.
Public communication: Transparently communicate with the public about the use and impact of AI systems.
Importance: Engaging stakeholders helps identify potential issues early and ensures that AI systems align with societal values and expectations.
Continuous Monitoring and Improvement
Performance monitoring: Continuously monitor AI systems for performance, accuracy, and compliance with governance protocols.
Feedback loops: Establish feedback mechanisms to learn from AI system performance and make necessary improvements.
Importance: Continuous monitoring and improvement ensure that AI systems remain effective, ethical, and aligned with governance protocols over time.
Incidence Response and Redemption
Incident response plans: Develop and maintain incident response plans to address issues that arise from AI system failures or misuse.
Remediation measures: Implement measures to correct and mitigate the impact of incidents when they occur.
Preparedness for incidents ensures that businesses can quickly and effectively respond to and recover from issues, minimizing harm and maintaining trust.
By establishing these AI governance protocols, businesses can ensure that their AI systems are developed and used responsibly, ethically, and in compliance with legal and societal expectations.
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Businesses must be aware of developing the AIGA (AI Governance and Accountability) program because it offers a framework for the responsible and ethical use of AI technologies. As AI becomes more embedded in business operations, ensuring these systems operate ethically and transparently is crucial. The AIGA program provides guidelines for ethical considerations, data privacy and accountability, helping businesses navigate the complexities of AI implementation.
Sharon (Shaghayegh) Shahrokhi Tehrani, Product Manager, Machine Intelligence Retention, CBC (Canada's Top 25 Women in AI)
Businesses that overlook ethical AI practices risk perpetuating biases and making poor decisions, potentially harming individuals and communities. Therefore, the AIGA program is vital for aligning AI initiatives with societal values and expectations.
Sharon (Shaghayegh) Shahrokhi Tehrani, Product Manager, Machine Intelligence Retention, CBC (Canada's Top 25 Women in AI)
Read the full interview here
Adhering to the AIGA program builds trust with stakeholders, including customers and regulators. Ignoring these guidelines can lead to reputation harm, regulatory fines, and legal liabilities.
Sharon (Shaghayegh) Shahrokhi Tehrani, Product Manager, Machine Intelligence Retention, CBC (Canada's Top 25 Women in AI)