The Importance of AI Data Governance and How You Can Get Started
According to Appen’s State of AI report, nearly three-quarters of businesses now consider AI critical to their success. With the growing interest in AI and the implementation of data collection across industries, there’s still one critical idea many businesses are missing: bad data equals bad results.
Your results from AI are only as good as the data you put in. If you’re not leveraging high-quality training data and managing your AI models with data governance, you’re wasting time and money by producing bad results.
This needs to change.
Why Your Company Needs AI Data Governance
AI data governance is the processes and rules used to guide and govern your company’s AI models. Without AI governance, your AI models lack oversight, might produce unnoticed errors, and could be rife with bias.
Defining AI Governance
AI governance is composed of three main areas: purpose, management, and governance.
- AI model purpose: Your AI model must have a clearly defined purpose that will guide what you want to achieve with your model’s data results.
- AI model management: AI model management is all about documentation and understanding the use cases of each model.
- AI data governance: Governance looks at the quality and provenance of data to ensure the best results.
New technology and the promises it makes are always tempting. Companies often jump in with excitement. Before you jump, be sure to have your AI data governance policies and best practices in place.
Best Practices for Creating Your Own AI Data Governance Policies
Without AI data governance, your team could be wasting their time and money. Here’s how you can create AI data governance best practices at your company and ensure you’re getting the highest quality results from your data.
Start Small
One of the easiest ways to start using AI technology and avoid problems is to start small. Start with one or two small models and projects. Use these models to create your governance, eliminate bias, and ensure you’re getting the results you’re looking for.
Create Policies to Avoid Bias
AI technology has a lot of potential benefits, but it also has the potential to harm. One important aspect to include in your AI data governance policies are ways to avoid bias and promote inclusion. Each AI model must be evaluated for its benefits and potential bias to ensure an inclusive experience for all users.
Document, Document, Document
Documentation is a critical step in AI data governance. Documentation ensures knowledge transfer, identification of stakeholders, tracking of data input and output, and notes any potential problems or issues that might arise. What metrics were used to define success? What changes were made to the model?
Stay Up To Date On News and Technology
AI data governance is new and is continuously evolving based on cutting-edge research. If you’re implementing AI technology in your business, it’s critical to stay up to date on news and policy changes at other, larger companies so you can ensure you’re not recreating the same mistakes.
Take Data Security Seriously
One of the reasons AI works is because there’s so much data widely available today. But, just because data is available to use doesn’t mean it’s ethical to use. No matter what type of data you’re inputting to your AI models, it’s important to take data security seriously and to protect your data from those who would misuse it.
As you’re developing your company’s AI data governance policies, be sure to lean on what’s already been created by organisations such as the World Economic Forum and the Allen Institute for AI. You can use templates and learnings from these organisations to create an AI data governance policy that makes sense for your business.
Author bio:
Wilson Pang joined Appen in November 2018 as CTO and is responsible for the company’s products and technology. Wilson has over 17 years’ experience in software engineering and data science. Prior to joining Appen, Wilson was Chief Data Officer of CTrip in China, the second largest online travel agency company in the world where he led data engineers, analysts, data product managers, and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering in eBay in California and provided leadership to various domains including data service and solutions, search science, marketing technology, and billing systems. He worked as an architect at IBM prior to eBay, building technology solutions for various clients.
Wilson obtained his Master’s and Bachelor’s degrees of Electric Engineering from Zhejiang University in China.
Katherine Holden
Katherine joined techUK in May 2018 and currently leads the Data Analytics, AI and Digital ID programme.