Responsible AI Relies on Data Literacy
Data literacy is a key component for any organization to be able to scale responsible and trusted artificial intelligence technology. Because of the multidisciplinary nature of AI products, stakeholders across an entire organization must share a common understanding of each project’s scope, deployment, governance, impact, and projected risk.
In addition to multi-stakeholder engagement, proper AI governance should include the adoption of a common framework and individual or group sign-off from personas in various departments, roles, backgrounds, and levels of technical proficiency. Achieving that level of governance at scale requires a common understanding of AI and data concepts. Individuals interacting with AI systems should possess a baseline data literacy, especially in high-risk use cases that require human collaboration at the final decision-making stage.
Data literacy ensures that diverse perspectives are baked into AI governance and leads to the production of AI systems that achieve better and more consistent outcomes. It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models.
What Is Data Literacy?
Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. Data literate individuals should be able to distinguish between various data roles, communicate insights from data, and derive data-driven decisions. This baseline of data literacy is particularly important for non-technical stakeholders who might not already be familiar with AI principles, since it enables them to apply their domain expertise to the process of developing, building, and implementing AI projects. Empowering subject matter experts to guide the development and governance of AI systems helps maximize value for end users and minimize potential harm.
How Can Organizations Cultivate Data Literacy?
Sharing data literacy across both technical and non-technical disciplines enables organizations to adopt a common data language that promotes mutual understanding. And beyond uniting diverse stakeholders, data literacy also empowers sectoral regulators to provide industry-specific guidance. In order to scale responsible AI, organizations should implement these fundamental building blocks of data literacy:
- The data science and machine learning workflow: Learning about the steps required to create predictions from raw data helps stakeholders develop an understanding of AI project implementation.
- The distinction between various data roles: Understanding data roles (i.e., data engineers, data scientists, machine learning engineers, etc.) and their contributions to AI systems facilitates smooth collaboration and mutual understanding of accountability.
- The flow of data through an organization: Mapping how data flows through an organization helps organizations get and stay aligned on potential bias risks with data collection and data degradation.
- The distinction between various types of AI systems: Distinguishing between various technologies (i.e., rule-based AI, machine learning, deep learning, etc.) allows stakeholders to evaluate the most suitable models for deployment and is paramount for organizations scaling and operationalizing AI systems.
- Evaluation metrics for machine learning models: Understanding evaluation metrics, what they optimize for, and how they intersect with AI fairness principles gives stakeholders the language necessary to qualify risks associated with AI systems.
Raising the level of data literacy in an organization is critical as more and more industries turn to AI. Data literacy empowers diverse stakeholders — regardless of their level of technical training — to assume ownership and accountability for their organizations’ AI governance charters. It also ensures that organizations have the skills necessary to be competitive in their industries, and empowers employees to make constructive contributions to comprehensive implementation checklists, define business rules, critically evaluate testing reports, and assess the risks associated with any given AI system.