Building an enterprise AI governance plan 

All AI systems are built on a foundation of data, which may include vast amounts of personal and sensitive information. Governance plans establish guidelines for data protection, ensuring that strong privacy measures are in place and that sensitive data isn’t being shared to unauthorized users. They also address security concerns, mitigating the risk of illegal access or malicious use of AI technologies.

Opacity
AI's greatest value—its ability to operate on its own without excessive human intervention—can also be its biggest failing. AI algorithms can be extremely opaque, making it difficult for human users to identify and validate the processes being used to arrive at certain decisions. Governance plans advocate for transparency, requiring AI developers and users to provide explanations for AI-generated outcomes. This promotes accountability and allows individuals to understand and challenge decisions made by AI systems.

Noncompliance
The AI landscape is relatively young, and the possible legal impact of intelligent automated systems is still being assessed. Even so, modern AI technologies may face legal restrictions requiring them to comply with existing laws and regulations. Additionally, as new legislation is introduced, existing systems will need to be adapted and monitored to confirm that they continue to operate within the boundaries of the law. AI governance plans help navigate legal complexities, ensuring that AI systems are developed and deployed in compliance.

Correctly implemented, AI governance plans can help organizations get more out of their AI tools without exposing themselves or their customers to unnecessary risk. To do this and to address the concerns listed above, AI governance plans must adhere to four principles: 

Fairness
Although AI-powered systems are not inherently discriminatory, biases can emerge from the individuals handling the machine learning and computer vision processes, or from training AI algorithms on narrow datasets that fail to capture the full diversity of human experiences. To address this, organizations must prioritize the use of more comprehensive and diverse datasets during the initial training phases. By doing so, AI can be educated to recognize and mitigate biases, fostering fairness and inclusivity in its outputs

Transparency
The more AI algorithms become involved in the day-to-day processes and decisions of enterprise business, the greater the risk of inaccurate or inappropriate responses. Users and stakeholders need to be able to understand and explain how AI systems arrive at their outcomes. Addressing the "black box" problem of AI, where decisions are made without clear explanations, requires the development of tools and approaches that enhance transparency. 

Data security and privacy
As previously stated, data security and privacy are crucial elements of AI governance. Organizations must build security into their AI governance framework to safeguard their data and protect clients' personal details from the risk of exposure. Strong cybersecurity measures should be implemented as part of the AI governance plan to protect the integrity and confidentiality of valuable data throughout its lifecycle. 

Human centeredness
As advanced as they are, AIs still primarily exist to interact with human operators and meet the needs of living users, which is why AI systems must be designed with a human-centered approach. Considering human requirements, psychological factors, and behavioral patterns makes AI more effective and helps eliminate many of the barriers for those who are less familiar with the inner workings of the algorithm. Whether used for automation or predictive analyses, AI technologies are more likely to be successful and accepted if they align with human goals.

While the principles of AI governance provide a crucial foundation for guiding AI governance planning, there are additional considerations that organizations must keep in mind. These considerations go beyond the principles and provide practical direction and essential best practices for designing effective AI governance plans.

Consider the following aspects when creating an AI governance plan:

Model performance management
Effective AI governance should include mechanisms for ongoing model performance management. AI models can exhibit performance degradation or biases over time, and regular monitoring is necessary to ensure their continued accuracy, fairness, and reliability. 

Organizations are encouraged to establish processes for continuous evaluation, feedback loops, and model retraining to maintain optimal performance. This involves tracking key performance indicators, identifying potential issues, and implementing corrective measures. A dedicated approach to performance management ensures that AI systems remain effective and aligned with organizational objectives while minimizing risks.

Extensive employee involvement
Creating AI governance requires active involvement from employees across different roles and functions within the organization. Collaborating with various stakeholders—including data scientists, engineers, domain experts, legal teams, and ethics committees—is crucial for developing comprehensive governance frameworks. 

By involving a diverse set of perspectives, organizations can gain valuable insights, identify potential ethical concerns, and create processes for proactively addressing those concerns. Employee involvement fosters a sense of ownership, accountability, and collective responsibility towards ethical AI development and use. Perhaps even more importantly, it brings ethical consideration to the forefront of employee consciousness. 

AI governance walks a fine line: On the one side, so few restrictions may allow the AI to run rampant, exposing sensitive data, concealing its internal workings, spreading bias and unconfirmed information, and opening up the company for potential compliance-related penalties. On the other side, an AI that isn’t allowed to operate on its own loses its primary advantage, making it all-but useless for modern organizations. 

Businesses need AIs that can provide the operational and decision-making functionality they were designed for, without running the risk of those AIs overstepping their bounds. AI systems can be applied constructively in many different tasks and processes, leveraging machine learning, natural language processing, computer vision, and other components to optimize ROI, streamline decision making, and drive innovation and productivity. To do this safely, however, the focus must be on identifying the appropriate levels of governance for different areas.

With a clear understanding of the principles that should drive the AI governance plan, and keeping other important considerations in mind, it’s time to begin the planning process. Building an effective AI governance plan requires a systematic approach that addresses the specific considerations and challenges associated with AI development and deployment; the following are essential steps to help ensure that the plan supports AI governance needs:

1. Define the purpose and the scope of the plan
The first step in building an AI governance plan is to clearly define what it will be expected to accomplish and how extensive it will become. Determine the goals and objectives of the plan, including the specific areas of AI technology and applications it will cover. This includes identifying the AI systems, algorithms, and data sources that fall within the scope of the proposed governance. Defining these elements provides a clear direction and focus for the plan moving forward, and may even reveal areas where AI governance isn't necessary.

2. Conduct risk assessment
At the end of the day, AI governance exists to protect businesses and customers from the risks associated with AI. As such, identifying and defining these risks is an essential early step in creating a governance plan. This stage involves evaluating the ethical, legal, and social implications of AI systems, including considerations such as bias, privacy, security, and accountability. By understanding the risks and challenges upfront, organizations can develop appropriate strategies and mitigation measures to address them effectively.

3. Establish ethical guidelines and principles
Next, develop a set of ethical guidelines and principles that align with the organization's values and the broader societal context. These principles should guide the development, deployment, and use of AI technologies, and will likely include considerations such as fairness, transparency, accountability, privacy, and inclusivity. Clearly articulating these guidelines helps foster a culture of responsible AI use within the organization. 

4. Involve stakeholders from across the organization
AI systems have the capacity to affect stakeholders at every level and in every part of the organization. Ensure that the development of the AI governance plan involves input and buy-in from the full range of disciplines and departments within the company. This includes data scientists, engineers, legal experts, ethics committees, domain specialists, and representatives from impacted user groups. Involving diverse perspectives helps capture a holistic view of the ethical and operational challenges associated with AI, fostering collaboration and ownership of the governance plan.

5. Define policies and procedures
Translate the ethical guidelines and principles into concrete policies and procedures that can be used to guide the use of AI systems. These policies should cover areas such as data acquisition, model development and validation, algorithmic decision-making, user consent, data privacy, and security. Clearly defined policies and procedures encourage consistent and accountable practices throughout the AI lifecycle.

6. Establish mechanisms for accountability and transparency
Even if AI is capable of operating on its own, it is the organization that is responsible for ethics and accuracy of its outputs. Define all of the roles and responsibilities associated with the AI system, establish mechanisms for auditing and monitoring AI systems, and implement mechanisms for explaining AI-generated outcomes. Organizations should be able to provide clear explanations for the decisions made by AI systems and be accountable for the actions of these automated tools.

 7. Constantly evaluate and improve
AI is always evolving, and organizations need to evolve their approaches to AI governance if they want them to remain viable and relevant. Build processes to monitor performance, impact, and adherence to the governance plan. Regularly assess the plan's effectiveness, gather feedback from stakeholders, and make necessary adjustments to address emerging challenges. This particular stage is ongoing, and will remain in effect throughout the life of the plan.

AI governance is an essential aspect of today’s governance, risk, and compliance (GRC) frameworks. As organizations strive to manage risks, operate within established regulations, and uphold ethical standards, integrating AI governance within the broader GRC framework becomes imperative. 

AI is changing the face of business, but at its heart it carries the same responsibilities and ethical considerations organizations have always had to operate within. Building an AI governance plan and incorporating AI governance into a larger GRC framework keeps your business secure and above board, and in a better position to put AI safely to work driving success.