Companies worldwide are increasingly exploring the use of artificial intelligence (AI) in almost every aspect of business, including customer service, business operations, and even human resources. Implementing these rapidly developing AI technologies can undoubtedly transform a business; however, as we advise our clients, these emerging AI technologies may expose companies to new privacy and legal risks.
Our previous blog post provided a comprehensive overview of the new EU AI Act (AI Act), a significant piece of legislation that we believe will serve as a model for AI regulation globally. In our series of AI-related posts, we will examine the chapters and articles that comprise the AI Act and how it may affect your business.
We begin with a core concept of the AI Act, AI literacy. Article 4 of the AI Act dictates that:
Providers and deployers of AI systems shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in, and considering the persons or groups of persons on whom the AI systems are to be used.
Article 4 emphasizes the importance of ensuring that those who create, operate, or use AI systems understand the technical aspects, potential impacts, and ethical considerations related to the development, deployment, and use of AI technologies. This requirement promotes responsible and informed use of AI by ensuring that those interacting with AI systems are equipped to handle them effectively and ethically. Accordingly, our clients are increasingly inquiring about the technical aspects of AI and related privacy and ethical concerns. In response, we provide the following overview.
What is Artificial Intelligence?
AI is a branch of computer science focused on creating and developing systems that can emulate human-like intelligence and behavior. It encompasses a wide array of technologies and methodologies, each contributing to the development of “intelligent” systems capable of learning, reasoning, and adapting to their environments.
How does AI work:
Rule-Based Systems: These systems operate on predefined rules and logic, making decisions based on specific conditions. They are often used in systems to mimic the decision-making ability of a human expert in fields like medical diagnosis or financial forecasting. These systems are designed for structured environments; however, their reliance on explicit rules can limit functionality.
Machine Learning: These systems are powered by algorithms, sets of mathematical rules or instructions that empower a machine to perform a task or solve a problem. With machine learning, algorithms are used to process data, perform calculations, and automate reasoning tasks. Algorithms are the core of machine learning as they are “trained” to become machine learning models.
Machine learning can be broadly categorized into several types, each suited to different tasks and data structures. These include:
Supervised Learning: In supervised learning, models are trained using labeled datasets, where each input is paired with a corresponding output. This paradigm is akin to learning with a teacher, as the model is guided by examples to understand the relationship between inputs and outputs. The goal is to learn a mapping function that can accurately predict the output for new, unseen data. Supervised learning is widely used in applications such as image classification, where models learn to identify objects in images, and in predictive analytics, where they forecast trends based on historical data.
Unsupervised Learning: Unsupervised learning, in contrast to supervised learning, deals with unlabeled datasets. Without predefined outputs, the algorithm must infer the natural grouping or organization of the dataset. Unsupervised systems focus on uncovering hidden patterns or structures within the data. This paradigm is particularly useful for clustering tasks, such as customer segmentation in marketing, where the goal is to group similar customers based on purchasing behavior.
Reinforcement Learning: Reinforcement learning is inspired by behavioral psychology and involves learning through interaction with an environment. In this paradigm, the algorithm learns to make decisions by receiving feedback in the form of rewards or penalties. The objective is to develop a strategy or policy that maximizes cumulative rewards over time. Reinforcement learning is instrumental in fields such as robotics, where it enables machines to learn complex tasks through trial and error, and in game development, where it powers AI agents to adapt and improve their strategies.
Deep Learning: Deep learning leverages neural networks with multiple layers to model complex patterns in data. These systems excel in tasks that involve large volumes of data and intricate structures, such as image and speech recognition. Deep learning models, like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have achieved remarkable success in applications ranging from autonomous vehicles to natural language processing.
Generative AI
Generative AI has fueled the current AI craze. If you are reading this blog, you have probably heard of ChatGPT and DALL-E, two generative AI technologies that have brought AI to the forefront of businesses around the world. As its title suggests, Generative AI can generate content such as text, images, music, and more while resembling the data they were trained on, producing new and original content. This capability is achieved through machine learning – particularly deep learning. Generative AI models undergo a training process where they learn from large datasets. During training, the algorithm adjusts its parameters to minimize the difference between generated outputs and real data, refining its ability to produce new content.
Privacy and Ethical Concerns with Artificial Intelligence
As companies increasingly integrate AI into their business processes, related privacy and ethical concerns grow. These include the following:
Data Collection and Usage: AI systems often require vast amounts of data to function effectively, which may include sensitive personal information. Additionally, some models restrict data internally only, whereas others rely on sending data outside the organization as part of a larger AI dataset. This raises concerns about how data is collected, stored, and used. Businesses should painstakingly review their data collection and use processes.
Consent and Control: Once AI systems collect data, individuals often have limited to no control over it. Ensuring that individuals have the ability to give informed consent and manage their data is crucial for protecting privacy rights.
Data Security: The storage and processing of large datasets make AI systems potential targets for cyberattacks. Breaches can lead to unauthorized access to personal information, resulting in identity theft, financial loss, and other privacy violations. Robust security measures are necessary to protect data from such threats.
Surveillance and Monitoring: AI technologies, such as facial recognition and location tracking, can be used for surveillance purposes, raising concerns about the erosion of privacy in public and private spaces. The potential for misuse by governments or corporations necessitates clear regulations and ethical guidelines.
Bias and Discrimination: AI systems trained on biased data can perpetuate or amplify existing biases, leading to discriminatory outcomes. This affects fairness and privacy, as individuals may be unfairly profiled or targeted based on biased algorithms.
Lack of Transparency: Many AI models, particularly deep learning systems, operate as “black boxes,” making it difficult to understand how decisions are made. This opacity can hinder accountability and make it challenging for individuals to know how their data is used or contest decisions that affect them.
Verification and Accuracy: Many AI models face data analysis and usage issues that may result in hallucinations. These hallucinations can lead to AI-generated data that is completely fabricated or illogical due to various factors. Thus, verifying output for logical consistency and accuracy is an ongoing concern.
Addressing the above privacy concerns requires AI system providers, deployers, and distributors to take a multi-faceted risk remediation approach that includes robust data privacy and protection processes, implementing ethical AI practices, and fostering transparency and accountability in the development and use of AI systems. By prioritizing privacy, businesses leveraging AI can mitigate risk and build trust with their employees and customers.
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