Protecting Privacy While Using AI

If the answer is yes to any of these questions this blog highlights five impacts of AI on privacy to consider as part of your project.

1. Data collection and surveillance: AI systems rely on vast amounts of data to train and make accurate predictions. This data often includes personal information, and the collection and analysis of such data can raise privacy concerns. For example, facial recognition technologies can be used for surveillance purposes, leading to the tracking and identification of individuals without their consent.
2. Data breaches and misuse: The increasing reliance on AI systems introduces new risks of data breaches and unauthorized access. If the personal data used to train AI models is not adequately protected, it can be exploited by malicious actors, leading to privacy violations and identity theft. Additionally, AI systems themselves can be vulnerable to attacks that result in unauthorized access to sensitive information.
3. Data retention and rights to erasure: AI systems and training data sets are subject to data retention periods like all other data repositories and should that data require deleting based on expiry or by request, there needs to be processes in place to identify the data, isolate it, and delete and or export and share it.
4. Profiling and discrimination: AI algorithms can create profiles of individuals based on their behaviour, preferences, and other characteristics. While this profiling can be used to provide personalised experiences, it can also lead to discrimination or biased decision-making. If AI algorithms are trained on biased or incomplete datasets, they may perpetuate existing inequalities and or influence automated decision making.
5. Inferences and sensitive information: AI systems can often make accurate inferences about individuals based on seemingly innocuous data. By analysing patterns and correlations, AI algorithms can deduce sensitive information that individuals may not have explicitly shared. This raises concerns about the unintended disclosure of personal details and the potential for manipulation or exploitation.
6. Lack of transparency and accountability: AI algorithms, particularly those based on complex deep learning models, can be opaque and difficult to interpret. This lack of transparency makes it challenging for individuals to understand how their data is being used and for what purposes. Moreover, it can be challenging to hold AI systems accountable for privacy breaches or discriminatory outcomes.

To address these privacy challenges, it is important to prioritise privacy-by-design principles, conducting privacy impact assessments, obtaining informed consent for data usage, ensuring data anonymisation where possible, and promoting transparency and explainability in AI algorithms.

Adhering to the growing number of privacy regulations whilst optimising the power of AI is a tricky balance which can be expensive when we lose sight of what the machines are actually doing!

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