Repurposing data means using personal information collected for one reason—like medical records, job applications, or patient forms—to train AI systems for other purposes. This often happens without the person knowing or agreeing. For example, photos or resumes sent for job applications might be taken and used to teach AI. These systems learn from large amounts of data and then use what they’ve learned for tasks like making decisions or recognizing voices.
Jennifer King, a privacy expert at Stanford University, says AI systems gather and use data on a huge scale and in ways people don’t easily see. Users have little control over what data is taken, how it’s used, or if it can be changed or deleted. This is especially hard in healthcare, where sensitive information is involved.
Using data for AI training without permission can cause many problems:
The U.S. has laws to protect civil rights, but these laws don’t always keep up with how AI works today. AI systems learn bias from the data they are trained on. This bias can lead to unfair results that hurt minority and Indigenous groups.
For example, facial recognition software sometimes misidentifies Black people. This has caused wrong arrests and mistakes by police. Such errors violate civil rights and make people lose trust in AI and public systems. These mistakes show how bad data can cause big problems.
AI hiring tools have shown bias too. Amazon’s AI recruiting system reportedly favored male candidates because it learned from past hiring data that was unfair to women. This shows how AI can repeat old inequalities instead of fixing them.
In healthcare, these risks are serious. AI used for making decisions about patients or resources can hurt vulnerable groups if the data is biased. Not telling people how their information is reused makes these problems worse.
Data privacy for Indigenous Peoples in the U.S. is an important issue. Indigenous data includes personal information plus cultural, community, and land details. Using this data without permission goes against Indigenous data sovereignty, which means the right of Indigenous communities to own and control their data.
M. Milad Khani, a legal researcher, says Indigenous data often gets used in AI training without asking or agreeing with the communities. This causes ethical problems and adds to AI bias and discrimination.
Although treaties protect Indigenous rights, there is little legal protection specifically for Indigenous data. This means Indigenous communities might have their data misused with no way to stop it.
Healthcare leaders working near Indigenous communities need to understand this. AI systems for patient care or health data should respect these rights and include Indigenous people in decisions about their data.
U.S. data privacy laws, like California’s CPPA, ask companies to respect people’s rights over personal data. But these laws often let people opt out after data is already collected. Many users don’t manage their consent regularly, and opt-out choices must be renewed every two years.
Jennifer King says it is better to change to an opt-in system. This means users must give clear permission before any data is collected or reused. Apple’s App Tracking Transparency (ATT) tool showed this works: most iPhone users said no to apps tracking their data.
This shift matters for healthcare because it lowers the chance of data being misused and can build trust with patients and workers. Also, following rules like HIPAA means protecting patient data properly.
Current laws in the U.S. and Europe mostly focus on making AI clear and fixing bias. But they don’t fully cover how data feeds into AI systems. To solve this, rules should cover both the data going into AI and the results AI produces. This helps stop AI from accidentally revealing private information or making bias worse.
Even though people have privacy rights, it is hard to use them with AI data use. Most people don’t have time or knowledge to see how their data moves across many groups. Opt-out steps can be confusing and must be repeated often.
Jennifer King suggests groups like data trusts can help. These groups work for people to manage their privacy. They can make better privacy protections than if each person tries alone.
Healthcare leaders in the U.S. might want to work with or support these groups. This can help protect patient rights while using AI to improve healthcare work.
AI tools like front-office phone automation are common in healthcare now. Companies like Simbo AI offer these to help with scheduling, answering patient questions, and managing work faster. This lowers human workload and wait times.
But these systems need access to personal details: patient contacts, appointment records, billing, or insurance info. If data is reused for AI training without care or permission, privacy breaches and unauthorized sharing can happen.
Simbo AI and similar companies should follow opt-in rules and clearly tell users how data is used. IT managers must set systems to follow privacy laws and check that AI makers only collect the needed data and use it just for the right jobs.
Important protections include:
When managed well, AI workflow tools can help patients and healthcare work without hurting privacy or rights.
Legal expert Rowena Rodrigues explains in the Journal of Responsible Technology that AI raises many legal and human rights questions. These include cybersecurity risks, lack of clear AI decision-making, bias, and problems with who is responsible for mistakes. Healthcare leaders who use AI should watch out for misuse of data, which can break privacy laws, cause civil rights cases, and lower patient trust.
Since AI systems can be “black boxes” where decisions aren’t clear, administrators should ask AI providers for:
Using AI responsibly in healthcare requires ongoing learning and staying alert to privacy rules and civil rights protections as they change.
Healthcare administrators, owners, and IT staff in the U.S. face new challenges with AI in healthcare. Using personal data for AI training without clear permission can harm patient and worker privacy, increase discrimination, and hurt civil rights.
Important points to remember are:
By keeping these in mind, healthcare groups can use AI well and keep the trust and rights of patients and employees.
Understanding how AI, data privacy, and civil rights connect is important for managing healthcare fairly. At the same time, organizations must balance better operations with strong data rules to make sure AI helps without hurting privacy or justice.
AI systems intensify traditional privacy risks with unprecedented scale and opacity, limiting control over what personal data is collected, how it’s used, and altering or removing such data. Their data-hungry nature leads to systematic digital surveillance across life facets, worsening privacy concerns.
AI tools can memorize personal information enabling targeted attacks like spear-phishing and identity theft. Voice cloning AI is exploited to impersonate individuals for extortion, demonstrating how AI amplifies risks when bad actors misuse personal data.
Data shared for specific purposes (e.g., resumes, photos) are often used to train AI without consent, leading to privacy violations and civil rights issues. For instance, biased AI in hiring or facial recognition causes discrimination or false arrests.
No, stronger regulatory frameworks are still possible, including shifting from opt-out to opt-in data collection to ensure affirmative consent and data deletion upon misuse, countering the widespread current practice of pervasive data tracking.
While important, these rules can be difficult to enforce because companies justify broad data collection citing diverse uses. Determining when data collection exceeds necessary scope is complex, especially for conglomerates with varied operations.
Opt-in requires explicit user consent before data collection, enhancing control. Examples include Apple’s App Tracking Transparency and browser-based signals like Global Privacy Control, which block tracking unless the user authorizes it.
It means regulating not only data collection but also training data input and AI output, ensuring personal data is excluded from training sets and does not leak via AI’s output, rather than relying solely on companies’ self-regulation.
Individual rights are often unknown, hard to exercise repeatedly, and overload consumers. Collective mechanisms like data intermediaries can aggregate negotiating power to better protect user data at scale.
Data intermediaries such as stewards, trusts, cooperatives, or commons can act on behalf of users to negotiate data rights collectively, providing more leverage than isolated individual actions.
Many current regulations emphasize transparency around AI algorithms but neglect the broader data ecosystem feeding AI. For example, even the EU AI Act largely ignores AI training data privacy except in high-risk systems.
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