Ethical AI usage is essential to prevent harm and ensure fairness. Proper data management practices help researchers identify and address potential biases in AI models, ensuring that results are ethical and equitable. Regularly assessing and adjusting AI models is crucial to identify and correct any biases. It is important to use datasets that are representative of diverse populations to minimise bias and ensure that the AI models provide equitable results. For example, in health research, AI models are often used to predict disease outcomes or recommend treatments based on patient data. If the data used to train these models is not representative of various demographic groups (e.g. age, gender, race), the model could generate biased results that favor one group over others.
To ensure sensitive data does not leak into AI tools, follow these best practices measures:
Use diverse datasets: Ensure that datasets used to train AI models are inclusive, representing diverse populations across ethnicity, age, gender, and other demographic factors. For example, health AI models should be trained on data that reflects the full spectrum of age, gender, and racial diversity to avoid biased outcomes.
Regularly update datasets: As society and demographics evolve, continuously update the datasets to reflect these changes, ensuring that the AI model remains relevant and fair.
Ethical Audits: Conduct regular ethical audits of AI research projects to ensure compliance with ethical guidelines. This includes reviewing data usage, model performance, and impact on different demographic groups.
Edith Cowan University acknowledges and respects the Nyoongar people, who are
the traditional custodians of the land upon which its campuses stand and its programs
operate.
In particular ECU pays its respects to the Elders, past and present, of the Nyoongar
people, and embrace their culture, wisdom and knowledge.