Can Artificial Intelligence And Privacy Ever Coexist?
Artificial intelligence has been a rapidly popular topic since around the start of the 2020s with the rise of chatbots such as ChatGPT, AI art generators such as Midjourney, and more. So the question of whether AI and complete privacy can coexist is more important than ever. The thing is that AI systems rely heavily on vast amounts of data to function efficiently. This reliance often raises concerns about privacy, data security, and the potential for the misuse of personally identifiable information.
What kind of data, you ask? Basically, large datasets of things such as text, images, and other data it can be trained on to learn, adapt, and improve their outputs. But this data also often includes personal information such as browsing habits, location data, and potentially, biometric details, especially in the case of virtual assistants powered by AI. The more data these systems have, the better they can understand patterns, make predictions, and offer personalised services. This dependency on this kind of data is at odds with the concept of privacy, which usually entails minimal or no data collection and maximum control over personal information.
In this case, there's a paradox where individuals want to enjoy the benefits of AI-driven services while also wanting to maintain their privacy. For example, users appreciate the convenience of personalised recommendations from streaming services, but often these conveniences come at the cost of sharing personal data. Despite all these challenges, there have been some technological advancements that have been developed to bridge the gap between AI and privacy, and they will be explained below.
For instance, federated learning allows AI models to be trained across multiple devices or servers holding local data samples, without exchanging the data itself. By keeping the data localised and only sharing model updates, federated learning enhances privacy while still enabling AI to learn and improve. Then there's differential privacy, where noise is added to data in a way that preserves individual privacy while allowing for accurate aggregate analysis. Differential privacy ensures that the output of an AI system does not reveal specific details about any individual in the dataset.
There is also the technique of homomorphic encryption, which allows computations to be performed on encrypted data without decrypting it first. Meaning that AI systems can process data and generate insights without ever exposing the raw data, thus maintaining some level of privacy. Another example of a privacy method would be zero-knowledge proofs, which enables one party to prove to another that a statement is true without revealing any additional information. In the context of AI, zero-knowledge proofs can be used to verify data integrity and authenticity without compromising privacy.
Governments have laws designed for data protection such as the European Union's General Data Protection Regulation and the United States' California Consumer Privacy Act, although their effectiveness regarding privacy is very debatable. There is also a call for the ethical use of AI, arguing that organizations must prioritise transparency, accountability, and user consent by designing their AI systems with privacy by default and privacy by design principles, so that these considerations are embedded in the development process from the outset. Organizations should additionally inform users about how their data is used and the measures in place to protect it. Educating the public about these technologies and their rights under data protection laws can empower individuals to make informed decisions about their data.
In conclusion, the coexistence of artificial intelligence and complete privacy is a challenging but not impossible goal, at least with all the information we know so far in this article. Although AI's reliance on data does pose an inherent privacy risk, technological advancements, robust policies, and ethical considerations can help mitigate these risks. Encouraging accountability and empowering users can also help realise the achievement of this goal.
A few links that helped with the writing of this blog post. Note that we are not affiliated with these websites. (if I were you I would copy and paste the info from these websites to a txt file before it gets moved/deleted)
https://medium.com/@th3Powell/privacy-and-artificial-intelligence-challenges-and-solutions-7c9278046647 (CLEARNET)
https://privacytools.seas.harvard.edu/differential-privacy (CLEARNET)
https://chain.link/education/zero-knowledge-proof-zkp (CLEARNET)
https://www.keyfactor.com/blog/what-is-homomorphic-encryption/ (CLEARNET)
https://research.ibm.com/blog/what-is-federated-learning (CLEARNET)
If you can't access these websites contact us right away and we will provide an excerpt from those websites! Or a similar website.
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