Chapter 8 Privacy Issues of AI This chapter sheds light on how private data is systematically collected, stored and analysed with the help of artificial intelligence. We discuss various forms of persistent surveillance at home and in public spaces. While massive data collection raises considerable ...
Data Privacy Vaults: The Ultimate Solution? So, is there an ultimate solution to overcome the privacy issues related to Generative AI? Innovative companies increasingly adopt data privacy vaults that isolate, protect, monitor, and manage sensitive user data and accommodate region-specific compliance...
Lack of awareness compounds these issues. Many Indians, particularly in rural areas, are unaware of digital privacy risks. This knowledge gap can lead to inadvertent data sharing and increased vulnerability to AI-driven scams and phishing attempts. ...
The data privacy concern when it comes to AI arises from the nature of the data itself as well as how the data is collected. In addition to the automated collection and ingestion of data from open sources, individuals may also directly input data resulting in unintended consequences, suc...
But one big question remains: How will you address the issues surrounding data privacy and trust? Companies are keen to tap into AI’s potential while navigating the complex terrain of ethical and secure data usage. Rest assured that these aren’t insurmountable challenges—they just require ...
On Monday, the European Union fined Meta roughly $275 million for breaking its data privacy law. Even though Meta’s violation was not AI specific, the EU’s response is a reminder that we need to build AI systems that preserve user privacy — not just to avoid fines but because we owe...
The rise of AI, however, has raises concerns among regulators worldwide prompting discussions about the need for regulation. Issues such as the potential for AI to surpass human control, algorithmic biases, the spread of misinformation, and the misuse of personal data have prompted questions about...
This is when your AI model moves from the training phase to the operational phase, where it’s extrapolating from new data. As your model nears production, review the inferences and predictions in its output. This is when you can check for accuracy, bias, and any data privacy issues. ...
Personal data from inputs and outputs can be used to help make the model more accurate over time via retraining. For AI projects, many data privacy laws require you to minimize the data being used to what is strictly necessary to get the job done. To go deeper on this topic, you ...
Another challenge is data privacy concerns. AI systems require access to large amounts of data to learn and improve their capabilities, and this data may include sensitive information such as customer data or intellectual property. This creates potential privacy and confidentiality issues, especially in...