Having a sound AI governance strategy has become imperative. Our previous blog discussed what AI governance is, why we need it, and the fundamentals of. Here we lay out three steps you can take to develop an AI governance strategy to stay competitive in a global economy, build the public’s trust in AI, and accelerate AI adoption in your organization.
STEP 1: Build a compelling business case for AI in your organization.
Business leaders typically evaluate a technology investment in terms of its return on investment (ROI). Investments in AI governance are no different. As a result, business decision-makers will want to present a compelling business case for how an AI governance strategy will reduce risk, improve growth and profitability, and facilitate goal achievement. Data scientists and technical experts who understand the power and benefits of AI in technical terms will want to show how an investment in AI governance benefits the bottom line. Therefore, AI governance leads, including technical experts, must clearly understand and define the critical problem(s) to solve with their AI governance strategy. They must also accurately assess the time and resources needed to design, stand up, and support a solid AI governance program that yields positive results for the business. Any assessment must also define and quantify the potential impacts, including risks and costs of NOT having a plan.
To make your case for funding AI governance in your organization, start with the following:
- Outline the scope of the AI governance strategy. Focus on realistic business outcomes you can commit to, potential opportunities, and your strategic priorities. Stay focused on the organization’s identified AI-related pain points, but make sure you can scale your AI governance strategy to the enterprise level as the use of AI in embedded systems expands.
- Define and detail desired outcomes. Estimate the value AI governance can bring to your business through better short and long-term risk management. Develop a realistic roadmap for implementing a standards-compliant governance program. Select an industry-standard framework and approach and best practices, and accurately estimate the cost of time and all resources required for your AI governance effort to succeed and be sustainable.
STEP 2: Your AI governance strategy should guide the development of AI embedded in applications, hybrid-cloud services, and solutions used across your ecosystem.
- Document how your organization will adhere to principles of responsible AI. List actions to ensure the fundamentals for good AI governance – transparency, interpretability, ethics, privacy, trusted autonomy, explainability – are met.
- Establish diversity requirements for your AI experts and contracted resources that mitigate the risk of bias in your data and AI models. Consider hiring a cross-functional team of subject matter experts, data scientists, technical experts, and analysts from diverse backgrounds, cultures, genders, ethnicities, age ranges, and more, who embody differing worldviews, perspectives, experiences, industries, and approaches to problem-solving.
- Define diversity requirements for algorithms, data sources, and data sets. Diverse algorithms fed with clean and varied data from trusted sources produce more ethical and accurate AI outcomes – and reduce risks.
- Thoughfully select best practices, governance frameworks, processes, and standards to guide AI users that are anchored around trust, transparency, and diversity.
- Develop robust policies and procedures that follow industry standards for using AI. Start with a few fundamental principles for using AI, then work toward principles that help your organization meet legal/compliance obligations and align with your organization’s core values.
Policies and procedures should meet these standards:
- Be intentional and compliant with your organization’s values related to safety, security, accuracy, and reliability
- Ensure the benefits of AI outweigh the risks associated with using it; utilize a valid cost-benefit analysis
- Be transparent and disclose usage of AI in your applications to stakeholders
- Establish tools, technologies, and roles to monitor and audit the results
- Thoroughly document all algorithms used in your systems and applications and all changes, including identities of team members who created the algorithm and made subsequent changes
- Ensure decisions using AI/ML can be explained with supporting data lineage and document the process for evaluating data quality, risks of bias, etc.
- Ensure AI models are not vulnerable to malicious manipulation or exploitation
- Monitor AI applications for inconsistencies and routinely test them against AI governance principles.
- Make your principles and plan available to your stakeholders.
STEP 3: Get guidance from an experienced AI governance expert.
Governance, explainability, and traceability requirements will likely differ for each AI application, as will security, privacy, and transparency points of view. Reveal offers the following advice:
- Consult with an experienced, technology-agnostic vendor that specializes in conducting unbiased AI governance assessments. Test your strategy and AI governance program as you would test for security or compliance gaps related to frameworks
- Ask if your vendor can securely monitor AI and ML models for performance, drifts, and the ability to interact with all possible data types at any level of volume without impacting performance.
- Ask what real-time and historical reporting and auditing capabilities are available and how customizable they are.
Datalytix regularly helps our clients develop AI governance strategies and advanced analytics solutions. Ask us how an AI Governance expert from Datalytix if you need advice or help with the following:
- Prepare an inventory of AI applications and use cases – from proof of concept through production.
- Develop a plan to ensure AI principles are applied in your organization, and that those not meeting pre-defined AI principles are retired.
- Develop a business case showing the benefits of an AI governance strategy.
- Define human roles and responsibilities to monitor (input, output, training data, etc.), analyze, and audit models for any biases/drifts.
- Identify and train AI experts within your organization to take AI governance work forward.
- Apply the governance strategy to innovative AI, advanced analytics, and data management technologies that solve current challenges.
