Manage Your Research Data: Compliance with Funding Grant Requirements

Sources: Clearly state where the data will come from (e.g., surveys, experiments, or third-party datasets). If AI tools generate data, it should be documented.

Ethical Considerations: Detail how data collection complies with ethical guidelines and privacy laws, such as the Privacy Act 1988. Consideration of ethical AI use is also critical, particularly when the data involves human participants or sensitive health information.

Ensure compliance with Australian privacy laws and regulations, such as the Privacy Act 1988. Data must be securely handled before it is input into AI systems, to prevent unauthorised access.

Specify the AI models and tools being used in the research, explaining how they will be applied to the research data.

Pre-Processing: Describe the steps to clean and transform data before using it in AI models, addressing issues such as missing data.

Quality Control: Explain how the accuracy, completeness, and consistency of the data will be maintained throughout the research process.

Bias Detection: Outline techniques for identifying bias in training data or AI outputs.

Bias Mitigation: Describe steps to reduce or eliminate biases to ensure fairness and prevent discriminatory outcomes in AI models.

Equitable Research Design: Show how AI tools and the research design will lead to fair and inclusive outcomes.

Transparency: Ensure that the AI models and their decision-making processes are interpretable and clear to both researchers and anyone else involved or interested in the research.

Accountability: Researchers must take responsibility for how AI tools are applied and ensure they align with ethical standards and funding guidelines.