The unauthorized use of personal data in AI training models can harm individuals in several ways. Firstly, it can lead to privacy violations and breaches, as sensitive information such as medical records, financial details, or personal communications may be used without consent. This can result in identity theft, financial fraud, or reputational damage for the individuals whose data has been misused. Moreover, the use of personal data in AI training models without proper consent can lead to biased or discriminatory outcomes. If the training data is not representative or includes inherent biases, the AI models may perpetuate and amplify these biases, leading to unfair treatment or discrimination against certain individuals or groups. Additionally, unauthorized use of personal data in AI training models can erode trust in technology and discourage individuals from engaging with AI-powered services or products, ultimately limiting the potential benefits of AI for society.
To understand the harm caused by unauthorized use of personal data in AI training models, imagine a library where librarians are tasked with organizing books based on readers’ preferences. However, instead of asking for permission, the librarians secretly gather personal journals and diaries of the readers to categorize the books. As a result, readers’ private thoughts and experiences are exposed without consent, leading to embarrassment and discomfort. Furthermore, the librarians’ biased categorization of books based on the personal journals leads to unfair treatment, as some readers are denied access to certain books. This breach of trust ultimately discourages people from visiting the library, depriving them of the opportunity to discover new knowledge and stories. Similarly, unauthorized use of personal data in AI training models can lead to privacy violations, biased outcomes, and diminished trust in technology, ultimately harming individuals and society as a whole.
Please note that the provided answer is a brief overview; for a comprehensive exploration of privacy, privacy-enhancing technologies, and privacy engineering, as well as the innovative contributions from our students at Carnegie Mellon’s Privacy Engineering program, we highly encourage you to delve into our in-depth articles available through our homepage at https://privacy-engineering-cmu.github.io/.
Author: My name is Aman Priyanshu, you can check out my website for more details or check out my other socials: LinkedIn and Twitter