Today we will talk about AI data Privacy. What comes to your mind when you listen to AI may not take your data privacy seriously. As we all know AI takes data and trains itself to problems. AI analyses all the present data Almost all AI systems employ a staggering volume of data to identify patterns and features of the data. Of course, this data can be labelled or unlabeled, and it can be provided to the program by developers, or gathered by the program itself. Let’s discuss how AI performe these tasks.
- How AI learns
AI systems apply intelligent algorithms to analyze the information and make knowledge from it. They can learn on their own as well as learn progressively.
- How AI performs tasks
AI systems apply the data which was received by the systems to perform different tasks. AI is useful if applied in various sectors which include healthcare and retail.
- How AI sources data
With the help of AI, information can be obtained both internally and externally. Indeed, internal data can be used if there is nothing more needed, however, if it is not enough one can use such information as third-party data providers, available datasets or web crawling.
These were the back story of how AI takes data. Now let’s discuss some challenges of data privacy.
Challenges in AI and Data Policy
There are certain challenges in AI and Data policy.
- Massive Data Collection: AI systems thrive on data. For the same, to train and optimize their algorithms, they depend on the Big Data containing the sensitive personal data. Thus, the problem of a large amount of collected information adds various questions regarding the storage, processing, and security of this data.
- Anonymization Limitations: Where most organizations employ anonymization approaches to safeguard individual identity, AI, for its analysis, can easily reverse the anonymization of data. This erodes the classical privacy protection approaches and makes people helpless.
- Bias and Discrimination: AI models can also actively incorporate flawed data feed from the training data. This does not only compromise the fairness in decisions made by AI but also compromises privacy by exposing attribute information that the individual might not wish to disclose.
- Data Ownership and Consent: Most of the time workers have little say as to what is done with the data collected. The consent procedures are often opaque or patronizing, meaning that users have no idea how their data powers AI tools.
- Cybersecurity Risks: Organization-wide bases of AI models and datasets are highly vulnerable to cyber threats. They can lead to the leakage of huge numbers of records that contain confidential information and thus pose a great risk to persons and entities.
These were the challenges let’s discuss their solutions below.
Solutions for AI and Data Policy
Having Identified Some of These Data Privacy Challenges Here Is How Those Challenges Can Be Solved
- Imposing Privacy-Preserving Approaches:
- Federated Learning: This approach adaptively trains artificial intelligence models on the users’ devices so the raw data is not pushed through central databases.
- Differential Privacy: This technique involves corrupting the datasets with noise and thus it is impossible to pinpoint an individual data point while the dataset still remains useful.
- Non-Hypocritical and Non-Snake-Oiling AI Solutions: Organisations must be able to guarantee the decentralised nature of an AI system. Transparency, through having clear documentation, explainable AI, and conducting checks at least periodically can go a long way.
- Enhancing the Legal Requirements: There is a requirement for government bodies and world organizations to implement better privacy laws such as the General Data Protection Regulation and the California Consumer Privacy Act. Ideally, these laws should be dynamic and adapt with the progress of AI systems as are in operation now.
- Improving Data Governance: Companies should adopt stringent data governance policies, including:
- Regular risk assessments.
- Encryption of sensitive data.
- Controlling accessibility to sets of data.
- Empowering Users with Control
- User Consent: Reducing the complexity of consent techniques applied and ensuring they are comprehensible and easily understandable.
- Data Portability: Allowing users to correct, export and delete their data with ease.
- Promoting AI Literacy
- Promoting AI literacy: The case study teaches people a lesson about AI and data privacy and makes them knowledgeable and capable of making a decision. A part of awareness campaigns can draw people’s attention to the need to protect their data or rather to the consequences of sharing information.
Conclusion
As we all know AI is still advancing and the creation of the algorithms must learn how to strike a balance between creation and privacy. Therefore, to promote the use of AI while at the same time respecting the right to privacy, more advanced technologies that protect the privacy of users must be encouraged, transparency increased and detailed regulatory laws developed. There is no doubt that only the cooperation of governments, enterprises, and civil society will enable the construction of an artificial intelligence world that will also take into account data privacy.
