Blog
Bridging the AI talent gap with collaborative AI governance tools
Though organizations are looking for leaders to head up their AI governance efforts, the talent pool of people with the knowledge and expertise to do so isn’t deep. Can a collaborative approach help bridge the gap?
Lauren Diethelm
AI Content Marketing Specialist
& Bex Evans
Senior Product Marketing Manager
April 23, 2024
On March 28, the White House Office of Management and Budget (OMB) released the first government-wide policy to mitigate the risks of AI, following President Biden’s Executive Order on AI last year.
The OMB policy directs multiple actions, including measures to address risks and increase transparency of AI use and to advance AI innovation. An additional – and notable – requirement is that under this policy, federal agencies will be required to designate a chief AI officer.
Though AI has been around for longer than the recent boom in interest would suggest, the fact remains that the pool of people who have the knowledge and skills to be a chief AI officer is limited. And as a result, this requirement under the OMB’s policy to mitigate AI risks is going to fuel the ongoing talent war for AI leadership.
At the moment, the OMB’s requirement for a chief AI officer applies only to federal agencies, but organizations in the private sector are facing similar gaps in AI expertise. To bridge this gap, many businesses are setting up AI governance committees to lead their AI governance programs.
While this committee approach has merit – diverse perspectives are critical when it comes to governing AI – it does underscore the need for technology solutions that facilitate collaboration among committee members and other stakeholders when adopting AI systems.
Why take a committee approach?
While private companies may see the OMB policy for federal agencies and rush to consider candidates for their own chief AI officer, it doesn’t, and maybe shouldn’t, need to happen that way. Before starting your headhunt for c-suite level AI talent, Gartner suggests that collaborative orchestration is a critical component of AI initiatives.
The broad applications of AI – and the depth and breadth of their risks and rewards – mandates a multidisciplinary approach to governance. As Gartner points out, there will be various AI initiatives happening across your organization that will require input and expertise from different areas; for the best chance of success, orchestrate them together. Establishing a central point of collaboration across diverse stakeholders will bring critical insights to your AI initiatives and their governance.
When you have an array of perspectives at the table, you’ll be able to surface opportunities to reduce redundant processes, accelerate risk mitigation, and streamline responsible adoption. There are a few key disciplines, and corresponding questions, to keep in mind when evaluating an AI system:
- Privacy: Is the system you’re building upholding privacy obligations and respecting individuals' choice?
- Fairness: Is the system built upon representative datasets and producing fair outcomes?
- Explainability and transparency: Is the system performing as expected and can behavior be explained to non-technical stakeholders?
- Safety and security: Is the system secure, safe to use, and robust?
- Validity and reliability: Is the model's performance monitored? Is there a mechanism in place for corrective action to be carried out when issues are identified?
- Accountability: Has a risk assessment been carried out? Have risks been appropriately documented and mitigated?
Ensuring that you have committee members from each of these different departments and jurisdictions will help make sure that they all get the attention and diligence they need; but it also means there will be a lot of seats at the table weighing in on your AI governance operations.
Putting it into practice
With so many people involved, creating a streamlined way to collaborate is key. Your committee must establish standardized collaboration points across every stage of the responsible AI lifecycle:
- Strategy and planning: This step involves identifying the business or societal problems to be solved with AI, defining the goals, and planning the project's scope and design in alignment with ethical guidelines and responsible AI principles.
- Data management: This step focuses on the collection, understanding, and preparation of data. It includes ethical considerations for data sourcing, exploration to uncover insights, and preparation to ensure quality and fairness.
- Model development: This involves selecting appropriate AI models based on the project's requirements, training the models with prepared data, and iteratively refining them. This stage emphasizes ethical AI practices, ensuring transparency and explainability of any models your committee decides to use.
- Evaluation and validation: This step is critical for assessing the model's performance, fairness, and reliability against predefined metrics. It includes rigorous testing to validate the model's effectiveness and its adherence to ethical standards.
- Deployment: This step involves integrating the AI model into the existing systems and deploying it into production while ensuring it operates within ethical boundaries and complies with legal regulations.
- Monitoring and continuous improvement: After deployment, continuous monitoring is essential to track the AI system's performance, impact, and ethical considerations. This step also includes optimizing the system for improved efficiency, fairness, and adaptability to changing conditions or new insights.
Each of these stages is focused on a different area, and requires different levels of expertise and reporting. Without a streamlined mode of collaboration, it becomes difficult to ensure your committee and other business stakeholders will track information or take action on insights in the same way.
Case study: Using AI for hiring
A key part of any AI governance committee is transparency, which includes internal documentation and reporting. As an example, let’s look at an organization that wants to develop an AI model for sorting resumes of prospective candidates.
In this scenario, there are two principles at work: data minimization and ethical considerations. Adhering to the principle of data minimization would encourage stakeholders to exclude certain data points, like gender and ethnicity, to limit the scope of sensitive data available to the model.
On the other hand, however, ethical considerations might guide the team to include those factors to bolster the model’s robustness and ensure that candidates with these characteristics are still eligible for the role.
Including data points like gender and ethnicity may be needed to measure the representation of different demographics across the entire population of a given dataset; from there, data scientists can analyze whether association with a particular group has a disproportionate impact on the outcome of the model.
In this scenario, the team would need to consider inputs from privacy, security, data, and ethics teams to effectively determine if this data is appropriate for this use case, and then ensure that they’ve properly documented all the context, risks, and decisions for the project.
This could potentially include:
- Risk assessments at the onset of the project, including a DPIA and an AI impact assessment
- Identified risks like exposure of sensitive personal information, algorithmic bias, and equal opportunity litigation
- Controls and evidence of controls including user access, DLP solutions, data robustness analysis, and algorithmic monitoring for fairness
- And procedures for acceptable use, malfunction, incidents, and decommissioning
This information helps create a clear record to assess and manage risks and to meet regulatory rules, so it is critical for a robust and working AI governance program. That said, the challenge for tech experts, like AI developers and data scientists, is that they must spend time writing detailed reports and explaining their work to different groups, instead of just focusing on building and improving AI models.
How OneTrust Helps
A solution like OneTrust’s AI Governance reduces the burden on technical stakeholders to provide inputs manually by integrating directly with common AI development platforms such as Azure ML, AWS SageMaker, Google Vertex and Databricks ML Flow, to supervise these environments. With these integrations, users can capture technical metadata, trigger actions, notifications and workflows, and federate AI governance policies.
In this way, OneTrust’s AI Governance acts as a unified program center for AI governance initiatives, so everyone in your organization, from your tech team to your business stakeholders, will have the tools they need to understand the intentional trade-offs that need to be made to achieve the responsible use of AI. This empowers informed decisions, minimizes the time and cost of AI system evaluation, and ultimately helps you build trust with customers and stay on the right side of regulations.
Speak with an expert today to see AI Governance in action and learn how it can work help streamline your AI operations.
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