AI and Privacy: The Price of a Smarter World
AI&Future |
As the wave of intelligence and digitalization sweeps the globe, artificial intelligence (AI) is ubiquitous: from smartphones to back-end systems in business operations, from self-driving cars to precision medicine, AI is profoundly changing our lifestyles. However, while this technological revolution brings convenience, it also poses unprecedented challenges to privacy protection. So, in this era of both opportunity and risk, can we still protect our privacy?
The widespread use of AI, particularly the new generation of intelligent systems represented by large language models (LLMs), is profoundly changing the relationship between humans and information. Compared to the traditional internet, AI requires individuals to proactively and continuously provide fine-grained data. This process, while seemingly a voluntary choice, is actually a difficult trade-off between convenience and privacy. Against this backdrop, personal privacy faces unprecedented risks. Protecting personal data in the AI era has become a major challenge facing society, law, and technology.
Confidential computing and fully homomorphic cryptography are seen as potential solutions to this dilemma. Companies like Apple, Microsoft, and Google are betting on both technologies, investing heavily in research and development.
Where does the privacy challenge arise?
AI relies on data-driven processes, and its powerful capabilities are fundamentally based on the collection and analysis of massive amounts of data. This means that whether using social media or wearing a smartwatch, our behavior, health status, and even personal preferences are recorded, providing data for AI models.
However, the collection and use of this data are often opaque, posing privacy risks in several ways:
- Excessive Data Collection:
Many applications do not clearly disclose what data they collect or why it is needed when obtaining user authorization. For example, a weather app might request access to your location, contacts, or even camera, far exceeding its core functional requirements.
- Algorithm Black Box Effect:
The complexity of AI algorithms leads to the so-called "black box" phenomenon: users are unaware of how AI uses their data or cannot even track how the data is processed. You might receive targeted ads without understanding why they "understand" your needs.
- Data Sharing and Abuse:
After data is collected, it is often shared across multiple platforms and even sold. Worse still, this sharing process may lack security, leading to frequent data breaches. For example, large-scale data breaches in recent years have exposed the information of hundreds of millions of users.
What privacy threats exist?
- Potential threats to privacy from surveillance
Smart connectivity enables real-time monitoring of human activities. This threat primarily involves product features such as cameras, fingerprint recognition, and voice recognition. Malicious attackers could potentially exploit the data collected by these products to link identities to specific individuals.
For example, a user's location could be recorded by a product or smartphone, and analysis could be used to create new information from the raw data, collecting and linking personal information. Furthermore, users may be unaware of the product's data collection, association, and real-time tracking. Faced with the challenges posed by smart connectivity surveillance, many manufacturers have responded by assuring users that their data will be anonymized. However, functionally, anonymizing personal data is difficult to achieve. IoT technology enables the connection of disparate data sets by linking data collected by individual products under certain conditions, enabling analysis and collection of disparate information. Even anonymized personal data can be re-identified when combined with other personal data.
- Automatic Identification and Inference May Lead to Discrimination
Various data from users' lives will be analyzed and processed by artificial intelligence components to extract useful information, achieve automated decision-making, and continuously improve set functions. However, conversely, this data analysis is performed through machine learning algorithms, which may lead to discrimination against users. Based on the collection and analysis of large amounts of data, it is easy to use algorithms to infer certain privacy aspects of users. In this case, malicious actors can use sensitive personal data to categorize users, leading to unfair treatment or even discrimination. If a user is seeking employment, and employers want to obtain and analyze data on relevant personnel to find the most suitable employees, personal data unrelated to the job application may have a significant negative impact on the user. Moreover, due to the opacity of the algorithm, users or regulatory agencies have little understanding of the decision-making process and find it difficult to influence or change decisions, leading to new forms of discrimination.
- The Informed Consent Principle Faces Dilemmas
The informed consent principle faces significant challenges in the field of smart interconnectedness. The characteristics of smart interconnected products bring difficulties to traditional privacy disclosure systems. First, privacy terms are difficult to find or understand. Data collection is ubiquitous, but in many cases, data subjects cannot read or understand the product's privacy policy. In an era of increasingly miniaturized products, the aggregation, analysis, and storage of personal data have become beyond the reach of users. Furthermore, the increasing complexity of products and the sheer volume of data they collect, coupled with the trend towards miniaturization, makes it difficult for manufacturers to provide easily understandable product descriptions and privacy policies. Even when manufacturers provide specific agreements and privacy policies, these accompanying legal provisions are generally lengthy, poorly worded, and difficult for most users to comprehend. Secondly, users essentially have no choice. Current smart connected service terms are typically drafted by manufacturers, offering little or no room for customization. Service and privacy terms are usually written and compiled by manufacturers, requiring data subjects to consent to receive services; users have no way to express their preferences regarding the purpose of using, processing, sharing, or retention of their personal data. Therefore, users often simply choose to accept the service, rendering informed consent meaningless.
In conclusion
The development of AI technology has brought many conveniences, but also many challenges. While enjoying the conveniences brought by AI, we must also be vigilant about its threats to personal privacy. Only in this way can we ensure the healthy development of AI technology and truly achieve the goal of technology serving humanity.
