Senior Content Marketing Manager II
December 1, 2023•14 min read
Artificial intelligence (AI) is a multi-faceted field focused on creating systems that mimic human intelligence—with the ability to learn, reason, and solve problems. AI models can be categorized into two fundamental types: predictive AI and generative AI.
Predictive AI makes predictions or forecasts based on existing data, analyzing historical patterns and data to anticipate future outcomes or behaviors. Generative AI, on the other hand, can create new data or content that resembles the input it has been trained—generating new content that didn't previously exist in the dataset.
AI models rely on massive data sets to learn, train, and evolve. So much so that generative AI tools wouldn’t be possible without the big data reality we live in today.Â
And when we say big data, we mean really big—with an estimated 2.5 quintillion bytes of data generated each day worldwide, the sheer scale of data available to train artificial intelligence is unprecedented. That in mind, the primary sources of data for AI tools are:
Beyond the various sources of data used to train AI, there are two main ways AI tools can collect data: direct and indirect.
AI tools generally employ one of two methods to collect data:
AI systems go through three fundamental stages when transforming raw data into actionable insights: cleaning, processing, and analyzing.
Privacy protections must be woven into each of these stages to ensure that the analytics process respects individuals' rights and follows legal requirements.
Transforming raw data into actionable insights is a crucial aspect of AI's functionality. AI systems utilize advanced algorithms and statistical techniques to identify patterns, trends, and associations in the vast sea of raw data. These patterns, once identified, form the basis for insights and predictions.Â
For instance, an AI system might analyze data from customer interactions to identify patterns in purchasing behavior. These patterns can be used to predict future purchasing trends, personalize the shopping experience for individual customers, forecast weather, and more.
It's important to note that the quality and diversity of raw data have a significant impact on the reliability of the insights and predictions. The more diverse and representative the data, the more accurate and useful the predictions will be.Â
In the context of AI, profiling refers to the process of constructing a model of a person's digital identity based on data collected about them. This can include demographic data, behavior patterns, and personal or sensitive information.Â
AI tools are remarkably adept at profiling, leveraging their advanced data processing capabilities to analyze vast datasets and identify intricate patterns. These patterns allow the AI system to make detailed predictions about an individual's future behavior or preferences, often with striking accuracy.Â
However, while profiling can facilitate more personalized and efficient services, it also raises significant privacy concerns.Â
AI-driven profiling can offer several potential benefits, primarily through personalized experiences and targeted services. For instance, profiling allows organizations to understand their customers, employees, or users better, offering tailored products, recommendations, or services that align with their preferences and behaviors.Â
However, AI-driven profiling also presents potential dangers. Aggregating and analyzing personal and sensitive data on a large scale can infringe on an individual's privacy and even threaten civil liberties.Â
For example, if an AI system learns from data reflecting societal biases, it might perpetuate or amplify these biases, leading to unfair treatment or discrimination. Similarly, mistakes in profiling can also lead to harmful unintended consequences, such as false positives in predictive policing or inaccuracies in credit scoring.
One of the primary concerns in AI is 'informational privacy' — the protection of personal data collected, processed, and stored by these systems. The granular, continuous, and ubiquitous data collection by AI can potentially lead to the exposure of sensitive information.
AI tools can also indirectly infer sensitive information from seemingly innocuous data — a harm known as 'predictive harm'. This is often done through complex algorithms and machine learning models that can predict highly personal attributes, such as sexual orientation, political views, or health status, based on seemingly unrelated data.
Another significant concern is 'group privacy'. AI's capacity to analyze and draw patterns from large datasets can lead to the stereotyping of certain groups, leading to potential algorithmic discrimination and bias. This creates a complex challenge, as it's not just individual privacy that’s at stake.
AI systems also introduce 'autonomy harms', wherein the information derived by the AI can be used to manipulate individuals' behavior without their consent or knowledge.Â
Taken together, these novel privacy harms necessitate comprehensive legal, ethical, and technological responses to safeguard privacy in the age of AI.
One of the most infamous AI-related privacy breaches involves the social media giant Facebook and political consulting firm Cambridge Analytica. Cambridge Analytica collected data of over 87 million Facebook users without their explicit consent, using a seemingly innocuous personality quiz app.Â
This data was then used to build detailed psychological profiles of the users, which were leveraged to target personalized political advertisements during the 2016 US Presidential Election. This case highlighted the potential of AI to infer sensitive information (political views in this case) from seemingly benign data (Facebook likes), and misuse it for secondary purposes.
Fitness tracking app, Strava, released a "heatmap" in 2018 that revealed the activity routes of its users worldwide, unintentionally exposing the locations of military bases and patrol routes. Strava's privacy settings allowed for data sharing by default, and many users were unaware that their data was part of the heatmap.Â
While Strava's intent was to create a global network of athletes, the incident underlined how AI's ability to aggregate and visualize data can unwittingly lead to breaches of sensitive information.
Facial recognition systems, powered by AI algorithms, have raised significant privacy concerns. In one instance, IBM used nearly a million photos from Flickr, a popular photo-sharing platform, to train its facial recognition software without the explicit consent of the individuals pictured. The company argued the images were publicly available, but critics highlighted the secondary use harm, as the images were initially shared on Flickr for a different purpose.
Regulators’ role in passing comprehensive privacy legislation has become increasingly critical with the rise of AI technology. Though this space is evolving rapidly, any regulatory framework should work to safeguard individual privacy while also fostering innovation.Â
Potential regulation could limit the types of data AI tools can collect and use, require transparency from companies about their data practices, and/or impose penalties for data breaches.Â
Legislators also need to ensure these laws are future-proof, meaning they are flexible enough to adapt to rapid advancements in AI technology.
Below are some of the prominent laws and proposals related to AI and data privacy:
Implemented by the European Union (EU), the GDPR sets rules regarding the collection, storage, and processing of personal data. It affects AI systems that handle personal information and requires explicit consent for data usage.
Enacted in California, this regulation gives consumers more control over the personal information that businesses collect. It impacts AI systems by necessitating transparent data practices and giving users the right to opt-out of data collection.
Several organizations and countries have formulated ethical guidelines for AI, emphasizing transparency, accountability, fairness, and human-centric design. These principles aim to govern AI's development and usage while safeguarding user privacy.
Various industries, such as healthcare and finance, have their own regulations governing AI and privacy. For instance, in healthcare, the Health Insurance Portability and Accountability Act (HIPAA) in the United States mandates data and privacy protection in medical AI applications.
These laws and proposals are only the beginning of legislative efforts to govern the use of AI. As the technology advances, more comprehensive and nuanced laws will likely be required to address the unique challenges it presents.
When developing AI technologies, various strategies and methods can be employed to ensure data protection. Below are some best practices.
By incorporating these methods into the development and operation of AI systems, organizations can better protect user data and ensure compliance with relevant data privacy laws.
Privacy Enhancing Technologies (PETs), including differential privacy, homomorphic encryption, and federated learning, offer promising solutions to data privacy concerns as artificial intelligence evolves.
These techniques and technologies have significant potential for enhancing privacy in AI and fostering trust in this emerging technology.
Implementing robust AI governance is pivotal to protecting privacy and building trustworthy AI tools. Good AI governance involves establishing guidelines and policies, as well as implementing technical guardrails for ethical and responsible AI use at an organization.Â
Organizations can adopt several measures to ensure the ethical use of AI:
1. Ethical guidelines will spell out the acceptable and unacceptable uses of AI, and should cover areas such as fairness, transparency, accountability, and respect for human rights.
2. Training and education will help employees understand how to use AI responsibly and ethically, and should include information on privacy laws and regulations, data protection, and the potential impacts of AI on society.
3. Transparency in AI involves clearly explaining how AI systems function and make decisions—helping organizations build trust with users and other stakeholders.
4. Accountability means that organizations should be accountable for the actions of their AI tools. This includes taking responsibility for any negative impacts these systems may have and taking steps to rectify them.
5. Regular audits and ongoing monitoring can be used to assess the ethical performance of AI technologies, identifying potential ethical issues and areas for improvement.
6. Stakeholder engagement can provide valuable insights into the ethical use of AI. This could involve seeking feedback on AI technologies and involving stakeholders in key decision-making processes.
7. Risk assessments help organizations identify, mitigate, and plan for the potential ethical risks associated with the use of AI.Â
By implementing these measures, organizations can ensure the ethical use of AI, reinforcing trust and confidence in their AI systems.
General privacy principles play a vital role in AI development, serving as ethical and legal guidelines for this emerging technology.Â
Applying privacy principles to AI systems is a multi-faceted process that involves defining ethical guidelines, collecting data, designing algorithms responsibly, and several rounds of validation and testing.
Through these steps developers can ensure they are being thoughtful when building and improving AI tools.
Continued dialogue and research in the realm of AI and privacy are essential. It is through these conversations and investigations that we can ensure ethical practices keep pace with technological advancements.Â
Whether we are developers, users, or policymakers, we must actively participate in shaping a future where AI serves humanity without compromising privacy. The journey to harmoniously integrate privacy and AI is only just beginning.Â
Let's continue to explore, question, and innovate, for in this pursuit lies the key to unlocking AI's potential responsibly and ethically.
Senior Content Marketing Manager II