Monday, October 2, 2023

Building an AI Governance Framework

By Apoorva Kasam - August 28, 2023 6 Mins Read

Building an AI Governance Framework

Companies must have an AI governance process to foster ethical commitments to AI. A robust AI governance framework ensures transparency and addresses data privacy issues.

As per a recent report by FTI Technology, “2023 Privacy and AI Governance Report,” 50% of firms with new AI governance frameworks are also building responsible AI governance with current and mature privacy programs.

Amidst the demand to develop and deploy AI apps, firms must consider and address the challenges of AI governance. Failure to address them can cause data privacy concerns, financial losses, and reputational damage.

FTI Technology’s report also states that 40% of firms are establishing algorithmic impact assessments with current processes for data privacy or protection impact assessments.

Why Companies Need an AI Governance Strategy

A solid AI governance strategy efficiently handles data quality and security. When properly implemented, it can build trust in data and systems at all levels within the company.

It boosts employee confidence in the decisions they make with data. These strategies also enhance accuracy in the AI models, resulting in better data quality. Proper alignment of ethics, management, and security minimizes reputational risk.

Approaches for Building an AI Governance Framework

  • Define Internal Governance Structures

Effective AI governance needs a defined internal business structure for better outcomes. Knowing how AI impacts innovations, productivity, and ROI is essential.

At the same time, the freedom to collate and use customer data has enabled firms to gather sensitive customer data. Now, they must define the roles to handle it.

Companies must provide the resources tools and guidelines to make them aware of AI governance and ethics. Here is how:

  • Embrace Top-down and Bottom-up Strategies

Every AI governance strategy must have strong leadership support. It enhances data quality, security, and management. Simultaneously, teams must take responsibility for the model, data, and tasks they manage.

It is essential to ensure continuous integration and cultural ownership of data issues. They can achieve a bottom-up strategy through efficient top-down communication.

  • Responsible AI

Tracking how the AI model makes decisions is vital as it directly impacts business outcomes. FITC Technology’s report states that 60% of firms have published responsible AI guidelines and 40% of them have not.

Moreover, tracking biases in opaque models is challenging. Hence, firms must set an approach to ensure the transparency of AI applications.

  • Set Operations Management

A mature AI governance framework must ensure the AI system aligns with the privacy rights to guarantee data security. Firms must identify potential security weaknesses and implement resilient measures. It helps prevent potential risks and adversarial attacks.

Checking and testing the AI systems enables firms to meet needs without compromising ethics and governance.

  • Better Model Management

Model management is a crucial factor in AI governance. As the model degrades over time, firms must timely check its performance and drift.

Continuous monitoring and testing ensure the model performance meets the company’s expectations.

  • Stakeholder Communication

Any AI governance framework must ensure transparency in communication with relevant parties. Firms must also develop policies governing AI and communicate with customers and stakeholders.

Stakeholders and customers must also know how AI works, its expected benefits, and outcomes. It will help them understand when and how AI will impact them.

  • Ensure Quality

Data scientists and business intelligence teams produce data products. But, these data products might lack the quality of traditional software development. Thus, companies must use code review, testing, and CI/CD to ensure high-quality data products.

Challenges of Building an AI Governance Framework

  • Varying Regulations and Customer Expectations Across Countries

Firms must deploy solid governance protocols when adopting AI. Effective regulation maximizes the benefits and minimizes the privacy risks of AI. But, considering varying AI regulations across countries is a challenge.

Disparate AI regulations make the scale and implementation of AI hard. Such regulations affect how companies respond to bias, safety, privacy, and transparency issues.

With regulatory expectations, firms must be aware of the rapid changes in the regulatory frameworks on AI. It will help them maintain policies and alter or modify the use of AI models.

  • Third-Party Technology Risks

The reliance on third-party apps for AI deployment is vital for scalability. But, it also needs the sharing of valuable company data. It can pose security threats to sensitive data privacy. It also hinders the tracking of relationship-lifecycle risks.

  • Health, Performance, and Safety

The three lines of defense for AI- health, performance, and safety- are vital for ensuring the system’s overall performance. However, training and awareness only aim at health- where data scientists only detect ethical issues.

Firms must also implement the other two lines of defense to ensure responsible AI. These help the AI systems to meet risk and compliance requirements. It prevents unnecessary exposure to privacy risks and impulsive decision-making.

  • AI-Human Partnership

Humans are at the core of AI system development. But, identifying their role is a challenging task. Moreover, there are issues about bias and errors among traditional human decision-makers. Knowing when people should intervene, and understanding their specific role in the collaboration process is also hard.

How Companies Can Address these Challenges?

  • Monitor and Manage AI Systems

AI system management and monitoring help detect data privacy and ethical issues early. This way, companies can enable remediation actions to minimize network downtime.

Having an oversight process can help address potential AI risks adequately. Moreover, establishing an inventory of all the AI systems and its use is a critical first approach.

  • Appoint Compliance, Fair, and System Governance Teams

Automated systems require manual reviewing to ensure that AI systems are unbiased and trustworthy. This manual review will be the first line of defense against discrimination and bias in AI systems.

Firms must employ fairness, compliance, and system governance teams to evaluate input variables. For an internal tech team, education and training can help them understand the responsible use of AI. For an external IT team, an active screening process helps ensure compliance.

  • Consider Regulatory Developments

Firms must consider external regulatory requirements for AI governance. Paying specific attention to regulatory developments can help prevent the risk of fines and penalties, and reputational harm.

  • Use Data Governance Tools to Address Risks

Companies must manage AI-based data to mitigate potential privacy risks. Data governance tools can help control the data. The tool automatically preserves privacy and helps comply with the rules and regulations of the area in which the firms operate.

Also Read: Components of AI Governance


With evolving AI tech, ethical and governance issues will also grow. As per a recent report by Allied Market Research, “AI Governance Market Forecast, 2021-2031,” the AI governance market will reach USD 2.7 billion by 2031, at a CAGR of 42.1%.

Firms must foster ethical AI to boost trust in the product and brand and drive consumer loyalty. It also helps prevent negative experiences with AI-enabled services.

Moreover, they must comply with ISO standards and guidelines, best practices, and laws. It ensures compliance with their AI governance models and frameworks.

Engaging with competent auditors and assessors periodically helps assess the effectiveness of governance and management of AI systems.

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Apoorva Kasam

Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.

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