Privacy and Artificial Intelligence - 1.4 Inadequate Consent Management and User Rights

1.4 Inadequate Consent Management and User Rights

Introduction

When people share personal information with others, they expect to know how it will be used and to have a real say in the process. In the digital world, artificial intelligence (AI) systems collect, analyze, and share lots of personal data, often without clear or meaningful user consent. Consent means giving permission, and in the context of AI, it means making sure people understand how their information will be used and that they have a real choice (Vinciworks, 2025). Without strong consent management, people’s privacy and rights can be at risk.

Technical or Conceptual Background

Historically, consent in digital environments was managed through simple checkboxes or “I Agree” buttons on websites. These traditional methods relied on users reading privacy policies and clicking to say yes, but these policies were often long and hard to understand (Velaro, 2024). Sometimes, users had to agree to everything at once, even if they only wanted to share a little bit. In some cases, websites would already have the box checked for you, or they would say that just by using the site, you were agreeing—even if you didn’t know it. Today, these ways are not considered good enough under modern privacy rules like the General Data Protection Regulation (GDPR) (Secure Privacy, 2025).

Now that AI and smart devices are everywhere, collecting information has become much more complicated. Devices like smart speakers, cameras, and phones can gather information quietly in the background, without clearly asking people if it’s okay (Secure Privacy, 2025). Privacy policies are still often written in tricky language, so people may not really know what they are agreeing to (Velaro, 2024).

Another problem is “consent fatigue.” If people are asked for permission too many times, they might stop paying attention and just click “yes” to get it over with (Data Dynamics, 2024). In places like homes or offices, sometimes only one person is asked for consent, but their choice can affect everyone else who uses the same device.

Current Trends and Challenges

New laws and rules are trying to help. For example, the GDPR in Europe and the AI Act require organizations to be more open and to give people more control over their personal data (Vinciworks, 2025). In Australia, new privacy laws and guidance from the Office of the Australian Information Commissioner (OAIC) require organizations to explain clearly how AI and personal information are used. This is supported by Australian Privacy Principle (APP) 1 and APP 5, which say that organizations must have clear privacy policies and give people easy-to-understand notices about how their information is collected and used (OAIC, 2024; Corrs, 2024).

Even with these new rules, there are still challenges. Many AI systems don’t give people clear ways to say yes or no to data collection, or to see and manage what information has already been collected (Velaro, 2024). Sometimes, even if people ask for their data to be deleted, it might still be stored somewhere else, like in a backup (Velaro, 2024). Consent choices are not always followed right away, so people’s wishes may not be respected immediately (Forbes, 2022).

To help with these problems, some organizations now use automated consent management systems. These tools use simple words and easy dashboards to help people understand and control their data (Secure Privacy, 2025). Privacy proxies are also being introduced as part of these solutions. Consent proxies allow users to delegate consent decisions to trusted entities or profiles that govern specific types of data or privacy concerns (proxyempire.io, 2023; datacritique.com, 2019). This means, for example, that a family could set up a trusted profile—like a parent or guardian—to make privacy choices for everyone in the household. The consent proxy follows the rules set by the trusted person or profile, so all the smart devices in the home will respect those choices. For instance, if the family wants to share only certain kinds of information, or only with certain apps, the consent proxy will make those decisions for them. This makes it easier for everyone, because you don’t have to answer the same questions over and over, and everyone’s privacy wishes are respected across all devices.

Mitigation Challenges and Shortcomings

Even with better tools and stricter rules, there are still problems. One big challenge is making consent easy but also meaningful. If it’s too easy, people may not think about their choices; if it’s too hard, they may give up (Data Dynamics, 2024). Making sure consent is respected right away is also tricky, especially for AI systems that work very fast (Forbes, 2022).

Some organizations use confusing or sneaky designs, called “dark patterns,” to trick people into saying yes without really understanding what they’re agreeing to (Forbes, 2022). For example, it might be much easier to say yes than to say no, or important information might be hidden in tiny print. This can break trust and even break the law.

In shared places, it’s hard to make sure everyone’s rights are respected. If one person gives consent for everyone, others might be affected without ever being asked (Secure Privacy, 2025). This is a tricky problem for families, workplaces, and public places where many people use the same devices or services.

Glossary

Term	Meaning and Example Sentence
Consent	Giving permission for something to happen. Example: “The app asked for my consent before collecting my information.”
Passive Data Collection	Collecting information without the user doing anything. Example: “The smart camera does passive data collection by recording all the time.”
Consent Fatigue	Feeling tired of being asked for permission so many times. Example: “Consent fatigue made her click ‘yes’ without reading.”
Privacy Policy	Rules about how personal information is used and protected. Example: “The privacy policy explained how the company uses your data.”
Opt-In/Opt-Out	Choosing to allow or not allow something. Example: “You can opt in to receive emails, or opt out if you do not want them.”
Dark Patterns	Tricky designs that make it hard to say no or understand choices. Example: “The website used dark patterns to make it hard to opt out of tracking.”
Privacy Proxy	A trusted helper that makes privacy choices for you or your group. Example: “The privacy proxy made sure my family’s smart devices all followed our privacy rules.”

Questions

1. What is consent, and why is it important for AI systems?

2. What are some problems with traditional consent models in AI and smart devices?

3. How do new laws in 2025 try to improve consent management?

4. What are two challenges that organizations face in managing consent for AI systems?

5. What are “dark patterns” and why are they a problem for user rights?

Answer Key

1. Suggested Answer: Consent is giving permission for something to happen. In AI, it means letting people know how their personal information will be used and giving them a real choice about it. It is important because it protects people’s privacy and rights (Vinciworks, 2025).

2. Suggested Answer: Traditional consent models often assumed users would read and understand long privacy policies, which was rarely the case. Consent was often bundled, making it hard to control specific data uses. Sometimes websites used pre-ticked boxes or said that just by using the site, you agreed—even if you didn’t know it (Velaro, 2024; Secure Privacy, 2025).

3. Suggested Answer: New laws in 2025, like the GDPR and AI Act, require organizations to be more open and to give users more control over their data. In Australia, organizations must follow Australian Privacy Principle (APP) 1 and APP 5, which say they need clear privacy policies and must tell people how AI and personal information are used (OAIC, 2024; AMI, 2024).

4. Suggested Answer: Two challenges are making consent meaningful without making it too complicated, and making sure that consent is respected right away so users’ choices are followed (Data Dynamics, 2024; Forbes, 2022).

5. Suggested Answer: Dark patterns are sneaky designs that make it hard to say no or understand choices. They are a problem because they can trick users into giving consent without really meaning to, which breaks trust and can break privacy laws (Forbes, 2022).

References

Data Dynamics. (2024, September 22). Navigating AI consent management: Data deluge & privacy. https://www.datadynamicsinc.com/blog-navigating-consent-management-in-the-age-of-ai-balancing-data-deluge-and-privacy/
Forbes. (2022, December 19). The Privacy Compliance Gap: How Lack Of Consent Enforcement Is Exposing Brands To Millions In Fines And Penalties. https://www.forbes.com/councils/forbestechcouncil/2022/12/19/the-privacy-compliance-gap-how-lack-of-consent-enforcement-is-exposing-brands-to-millions-in-fines-and-penalties/
Secure Privacy. (2025, March 19). Consent Management Challenges in IoT Devices. https://secureprivacy.ai/blog/iot-consent-management
Velaro. (2024, November 15). The Impact of AI on Privacy: Protecting Personal Data. https://velaro.com/blog/the-privacy-paradox-of-ai-emerging-challenges-on-personal-data
Vinciworks. (2025). What to expect from AI & GDPR in 2025. https://vinciworks.com/resources-files/gdpr/What-to-expect-from-AI-and-GDPR-in-2025.pdf
Office of the Australian Information Commissioner (OAIC). (2024, October 15). Guidance on privacy and the use of commercially available AI products. https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products
Corrs Chambers Westgarth. (2024, October 29). The crossroads of AI and privacy compliance: OAIC publishes new guidance. https://www.corrs.com.au/insights/the-crossroads-of-ai-and-privacy-compliance-oaic-publishes-new-guidance
Two Birds. (2025, February 25). Australia’s Privacy Regulator releases new guidance on artificial intelligence. https://www.twobirds.com/en/insights/2025/australia/australias-privacy-regulator-releases-new-guidance-on-artificial-intelligence
AMI. (2024, October 11). How do the Australian Privacy Principles (APPs) apply to AI? https://ami.org.au/knowledge-hub/how-do-the-australian-privacy-principles-apps-apply-to-ai/
proxyempire.io. (2023, July 24). Influence Of Proxies On Data Privacy And GDPR Compliance. https://proxyempire.io/proxies-data-privacy-gdpr-compliance/
datacritique.com. (2019, June 24). Could consent proxies help us navigate privacy concerns? https://www.datacritique.com/privacy/consent/2019/06/24/consent_proxies.html