Examine the privacy and security principle
AI systems should be secure and respect privacy. The machine learning models on which AI systems are based rely on large volumes of data that may contain private information. Even after the models are trained, and the system is in production, privacy and security must be prioritized. As the system uses new data to make predictions or act, both the data and decisions made from it may be subject to privacy or security concerns.
As AI implementation in education becomes more prevalent, it is important for you to be aware of how you can protect your learners' privacy and secure their personal details. Here are some key points to keep in mind when utilizing AI to keep private data secure:
Ensure that the AI systems you use comply with relevant data protection, privacy, and transparency laws. The AI system should have processes in place to continually check whether it is satisfying all aspects of laws in the localities where you will be implementing the AI system.
Ensure that the AI system is designed to maintain the integrity of your learners' personal data. The system should only use personal data for the defined purposes that have been communicated to the learners and should immediately delete any data that is inadvertently collected or irrelevant.
Use an AI system that is designed securely. The AI system should have role-based access controls and should protect your learners' confidential data when it is transferred to third parties.
Look for an AI system that provides appropriate controls for you to choose how and why your learners' data is collected and used.
Ensure that the AI system maintains your learners' anonymity by removing information from your data that could be used to identify an individual.
Consider using an AI system that has undergone privacy and security reviews and that implements industry best practices for tracking, accessing, and using learners' data.
Human-centered governance in utilizing AI
In addition to implementing technical protections, it is imperative to adopt responsible governance practices while leveraging AI capabilities. You can employ the following methodologies to implement human-centered governance:
Conduct impact assessments. You should conduct detailed privacy impact assessments on all AI systems you use to discover any potential issues early. Privacy impact assessments thoroughly evaluate how you collect, store, use, and share data to identify risks and guide mitigation strategies.
Employ stringent controls on personal information. You should assign risk levels to your learners' data and apply more stringent controls to any sensitive data such as photos and addresses. Less risky data, such as course reviews, can be assigned a lower risk level.
Implement controls on data usage. You should be transparent about how you and the AI system use learner data. You should also provide clear communication on how your learners can access, edit, or delete their data. Also, learners should be able to opt in or out of data sharing. These approaches will help to promote transparency and trust between you and your learners.
Monitor data practices. You should implement continuous monitoring of systems and data practices after implementing AI systems to identify emerging risks. Monitoring can include auditing an AI system's log files, observing the system for irregularities, and proactively probing for issues.
Address data breaches. You should implement procedures to responsibly address data breaches through notifications to learners and prepared mitigation plans. Any incidents should be thoroughly analyzed to prevent recurrence and improve the security of AI systems and data. By taking these steps, you can help to ensure the ongoing safety and security of your systems and data.
Implement security teams. You should appoint dedicated teams to manage data privacy and security. Key functions should be centralized to ensure consistency in data privacy and security measures.
Having established a foundation in privacy and security, you are now prepared to learn the principle of inclusiveness. In the next unit, you will navigate the necessity for AI systems to be fair, accessible, while also being attuned to the vast array of human experiences. It's about guaranteeing that AI is accessible to all, irrespective of their background or capabilities, enabling every individual to leverage AI to enhance their personal and communal well-being. By confirming that AI systems are not only just and reliable but also inclusive, we nurture a climate of trust and reliability in AI systems.