What's Happening?
Higher education institutions are being advised to prioritize data governance as a foundational step in deploying effective and trustworthy artificial intelligence (AI) systems. The emphasis is on understanding
the data they possess, including its location, ownership, classification, security, and maintenance. Poor data governance can lead to issues such as inconsistent data definitions and unsecured data, which can result in unexpected and potentially dangerous outcomes when used in AI models. The article highlights the importance of having a solid data governance framework to ensure that AI tools and models are built on accurate and reliable data. This approach is crucial for developing custom AI solutions in higher education, such as student success models and personalized advising, where the quality of data directly impacts the effectiveness of AI predictions and decisions.
Why It's Important?
The focus on data governance is critical as it directly affects the reliability and trustworthiness of AI systems in higher education. Poor data quality can lead to inaccurate predictions and decisions, which can have significant consequences for students, such as misaligned course recommendations or missed retention signals. By establishing robust data governance practices, institutions can ensure that their AI systems are built on a solid foundation, leading to better outcomes and increased trust in AI tools. This is particularly important in higher education, where decisions based on AI predictions can have a profound impact on students' academic and career paths. Additionally, effective data governance can enhance the return on investment for AI tools by ensuring that users trust and adopt these technologies.
What's Next?
Institutions are expected to continue developing and refining their data governance frameworks to support the deployment of AI systems. This includes addressing issues such as data classification, security, and access controls to ensure that sensitive data is protected and used appropriately. As AI governance becomes more integrated into institutional practices, there will likely be increased scrutiny on how AI models are trained, evaluated, and used. Institutions may also focus on creating a single source of truth for data definitions and expectations to eliminate confusion and improve data quality. These efforts will be crucial in building trust in AI systems and ensuring that they are used effectively to support educational goals.
