“Data is the new Oil” – this has been a (cliched) reality for over 2-3 decades now. Now, with the introduction of AI into data generation and processing, the value that can be extracted from data has increased tremendously. This has come with dangerous pitfalls – unwanted and uncontrolled invasion of privacy. Are we to conclude that Privacy is now no more than a fallacy?
The benefits of “mining data” have been capitalized for long, with very promising yields. It is not surprising that many organisations have structured their products’ / solutions’ offerings around data-based analytics, increasingly using various AI models. AI systems are, by their very design, data-driven—allowing them to learn and evolve in a way that makes the technology compelling to businesses and individuals alike. AI has shown remarkable efficiency in extracting values out of ‘banal’ data. However, this reality comes with significant risks, especially pertaining to privacy.
AI models rely on massive data sets to learn, train, and evolve. So much so that some of the newer AI types such as generative AI tools wouldn’t be possible without the big data. And big data is really big—with an estimated 2.5 quintillion bytes of data generated each day worldwide, the sheer scale of data available to train artificial intelligence is unprecedented.
AI’s strength lies in its ability to extract meaningful information from raw data. By employing sophisticated algorithms, AI systems can uncover hidden patterns and trends. These insights are invaluable for making informed decisions, such as predicting customer behavior, personalizing marketing campaigns, and forecasting future events.
However, this data also contains sensitive information that individuals may not want to share or organisations have used without their consent. That is where privacy regulations come in to rein in data exploitation and redress privacy harms but have fallen short because of a few underlying fallacies about how information ecosystems work.
AI’s potential for privacy violations extends beyond traditional concerns. The granular data collected by AI systems can be used to infer sensitive information, such as sexual orientation, political views, or health status, through predictive modeling techniques. This practice, known as predictive harm, can lead to discrimination and unfair treatment.
Moreover, AI’s ability to analyze large datasets can perpetuate existing biases and stereotypes. By identifying patterns in data, AI systems may unintentionally reinforce discriminatory practices, leading to algorithmic discrimination. This is particularly concerning for marginalized groups who may face disproportionate harm.
In addition to individual and group privacy concerns, AI also introduces the risk of autonomy harms. The information derived by AI systems can be used to manipulate individuals’ behavior without their consent or knowledge. For example, targeted advertising and personalized recommendations can subtly influence choices and preferences.
Addressing these multifaceted privacy challenges requires a comprehensive approach. Legal frameworks, ethical guidelines, and technological safeguards are all essential components. Governments, businesses, and individuals must work together to ensure that AI is developed and deployed in a responsible manner that protects privacy and promotes fairness.
