Ethical issue associated with AI
Biases: The output of AI is entirely rely on its training data, the prompts it receives, the engineers who develop the model. Consequently, both intentional and unintentional biases, whether explicit or implicit, can manifest within AI systems.
Copyright: To train an AI model, vast quantities of training data is required and is sourced from the internet, including copyrighted content. Such unauthorized use constitutes copyright infringement.
Privacy: privacy concerns persist regarding how AI systems collect personal data from users. Although some information, such as phone numbers, is provided voluntarily, users may be unaware that their IP addresses and online activities are also being harvested. This is particularly pertinent in educational settings, where students may be uncomfortable with their personal data being tracked and stored.
Using AI tools ethically includes following principles and practices that prioritize fairness, transparency and respect privacy in the use of AI systems.
Fairness in AI refers to ensuring that algorithms and models do not discriminate against individuals or groups based on factors such as race, gender, or socioeconomic status. This involves careful consideration of dataset composition, algorithmic design, and evaluation metrics to mitigate bias and promote equitable outcomes.
One crucial aspect of ethical AI use in academia is transparency. Transparency involves providing clear explanations of how AI systems make decisions and operate, helping users to understand the methods, data sources, and algorithms used in their AI-driven studies. This transparency helps building trust within the academic community and allows for confirming the reproducibility of results. Moreover, transparent reporting enables others to build upon previous work, advancing the collective knowledge base.
Privacy is another fundamental consideration in the ethical use of AI. Ensuring privacy in AI applications involves safeguarding individuals' sensitive information from unauthorized access, misuse.
In addition to safeguarding personal data, it's crucial to exercise caution when inputting information into generative AI systems, especially sensitive private data or copyrighted information. Generative AI models, such as text or image generators, have the capability to synthesize new content based on the input they receive. Therefore, inputting sensitive or copyrighted information into these systems can pose risks such as unintentional disclosure of confidential data or infringement of intellectual property rights. Ethical use of generative AI entails conducting thorough risk assessments and adhering to legal and ethical guidelines to prevent misuse or unauthorized dissemination of sensitive information.
