Using critical digital literacy skill to evaluate online information related to AI privacy concerns.
What are AI privacy concerns?
Issues with AI privacy include how AI systems handle, store, and process personal data. These worries include collecting data without permission, not protecting data well enough, possible hacks, and using private information in the wrong way. AI systems, especially those that use generative AI and process a lot of data, can put people’s privacy and data protection at great risk.
Shelby Hiter (2024)
Why is it an issue?
Unauthorized Data Use: AI systems may use personal data in ways that users haven’t authorized, either by accident or on purpose. For example, they may include sensitive data in training datasets or results
Lack of Regulation: There aren’t many laws that say exactly how data should be handled or protected in AI systems, so many AI technologies work in a regulatory grey area.
Data Breaches: AI systems can have security holes that let people who shouldn’t have access see personal information.
Opaque Practices: AI vendors may not be clear about how they store and use data, which makes it hard to know how personal data is treated and kept safe.
Risks to Ethics and the Law: Problems like stolen intellectual property and secret biases can happen, which brings up ethical and legal questions about about how AI affects privacy.
Shelby Hiter (2024)
Who is affected by AI privacy concerns?
Consumers: People who use AI technologies may have their privacy violated because their personal information may be gathered, saved, or utilized in inappropriate ways.
*Celebrities:
Scarlett Johansson, an actress, has sued OpenAI, saying that the company used her voice for its talking AI system without her permission. OpenAI CEO Sam Altman asked Johansson to give her voice to the AI system, which is what started the fight. Even though she said no at first, OpenAI showed off a model with a voice that sounded a lot like Johansson’s in the movie “Her”.
Pragati Pate (2024)
Business: To protect their image and follow the rules, businesses that use AI for data processing, customer interactions, or operational efficiency must also deal with privacy issues.
AI Vendors: They must effectively manage data handling in their systems and comply with changing rules.
Regulators and Policymakers: The people and groups that make and enforce privacy laws are affected by AI technologies as they try to solve their unique problems.
Shelby Hiter (2024)
How AI privacy concerns occur?
Web scraping: AI systems often get information from the internet that is open to everyone, like user records and content. If this isn’t done correctly, it can cause privacy problems.
AI models may keep and user inputs for future training, which could lead to private information being made public.
Biometric technology: AI systems that use biometric data (like facial recognition) can gather and record personal information about people without their permission, which is an invasion of their privacy.
Collecting data from IoT and APIs: AI systems can collect data from IoT devices and APIs, and this data is often added to larger datasets that may contain private or sensitive data.
Public records and polls: Data from public records and user polls maybe used in AI models, sometimes without the full understanding or agreement of the users whose data will be used.
Shelby Hiter (2024)
How to solve this problem?
To ensure the safety of AI systems, stricter rules for data management and safety are necessary. These include investing in high-tech security equipment ,using data masking and anonymization methods, and using synthetic data to create realistic data sets without personal or private information.
Clear and easy-to-understand data policies should be made by AI vendors and developers, outlining the data’s origin, collection, and storage. Regular data audits are also necessary to identify security vulnerabilities and improve data handling practices. Shortening data retention periods can help meet privacy protection goals by reducing the risk of unauthorized access or breaches.
Shelby Hiter (2024)
Why I chose this evaluation method to critically evaluate my source?
The CRAAP method helps me choose high-quality, trustworthy sources by making me think about them critically. This is important for doing accurate and reliable research.
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