Causal AI: Pioneering the Next Era of Artificial Intelligence

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Artificial intelligence, machine learning, and large language models have gained significant attention in the technology world. However, concerns regarding their limitations have arisen. While AI proves useful for simpler tasks like grammar correction, it faces challenges when dealing with complex problems. These limitations stem from the reliance of LLMs on derived patterns, which can result in inaccurate or irrelevant responses. Causal AI is viewed as a potential remedy to overcome these shortcomings and offer more dependable insights into AI-generated responses.

While artificial intelligence (AI), machine learning (ML), and large language models (LLMs) continue to dominate the technology landscape and generate ambitious predictions, concerns have arisen regarding their operational mechanisms. AI is expected to offer solutions to various problems, but it has encountered limitations, particularly in complex domains like disease management. A recent example is when ChatGPT provided incorrect advice regarding breast cancer treatment.

Nevertheless, AI can still prove beneficial for relatively simpler tasks such as sentence editing and grammar correction. The core issue lies in the functioning of LLMs, which consume data and derive patterns. When presented with a question that appears to match these derived patterns, the LLM responds based on them, potentially yielding incorrect or inapplicable answers for specific problems.

For example, although LLMs may possess a multitude of treatment methodologies for liver diseases, it would be unwise for a doctor to rely on their responses when treating a patient.

Causal AI has been proposed as a potential solution to the limitations of LLMs and AI. Experts believe that incorporating causal AI can address these challenges and provide more reliable insights.

What Is Causal AI?

LLMs consume vast amounts of data and analyze it to identify patterns. When you pose a question related to these patterns, their responses are based on the recognized patterns rather than your specific query.

An LLM might possess numerous patterns regarding road accidents in the U.S. and their potential remedies. However, if you inquire about preventing road accidents in a remote village along the border of Mexico, the LLM can only make predictions rather than provide accurate answers. These responses should be taken with caution, as the LLM lacks knowledge about the unique circumstances that lead to accidents in that specific location.

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Furthermore, you may not be aware of the underlying basis for the LLM’s responses, including the data, algorithm, and other factors.

Causal AI provides insights into how an LLM processes prompts or questions and generates responses. It aims to uncover the inner workings of an LLM as it consumes data, identifies patterns, and formulates answers. Experts believe that causal AI has the potential to enable the identification of acceptable responses from an LLM.

Currently, LLMs do not disclose the specific mechanisms through which they provide responses or reach outcomes, such as approving or rejecting a loan application at a bank. Consequently, it remains uncertain whether the basis for a denial, if any, is influenced by factors such as race, gender, or community. This lack of transparency in AI systems has led to significant criticism.

However, the emerging field of causal AI holds promise in addressing these concerns and bringing about greater transparency in the functioning of AI systems.

How Can Causal AI Be the Next Level of AI?

AI has encountered challenges in isolating and comprehending problems within their specific contexts. For instance, if you present a recurring issue with the gearbox of a particular car brand, AI might offer a solution based on its understanding of gearboxes across all vehicle brands.

However, this solution is unlikely to be effective as it fails to consider the unique circumstances of the problem.

The user is unable to verify the underlying rationale of the response or whether the suggested solution should be implemented. Incorrect implementation can have severe consequences. Consequently, there is a crucial need to verify the basis of AI tool responses before putting them into action, and causal AI can assist in this regard. It provides a transparent foundation for an AI response, allowing you to evaluate its merit.

Here are some examples of situations where causal AI can help:

  • Loan Application Evaluation: Causal AI can help determine whether a bank should approve or deny a loan application, providing transparent reasons for the decision. This can help prevent discriminatory outcomes based on factors such as race or community, as the AI’s decision-making process can be analyzed and verified.
  • Engine Horsepower Decision: While predictive analysis may suggest increasing the horsepower of a car’s engine, causal AI can delve deeper and consider the broader consequences. It may identify that a higher horsepower could result in increased fuel consumption and higher greenhouse gas emissions.

Why Is Causal AI More Powerful than AI?

A few examples prove that causal AI is more effective in predicting specific events than AI. Let’s discuss those events.

Incarceration Rates in the U.S.

In the case of using AI-based recidivism scores to determine sentencing, there is a potential for inherent flaws and discrimination. Traditional AI models may rely solely on correlational patterns, such as associating higher crime rates with areas predominantly inhabited by specific racial populations. However, this correlation does not necessarily imply causation, and using such data as a basis for decision-making can lead to discriminatory outcomes.

Causal AI, on the other hand, can delve into the underlying causal factors contributing to crime rates and recidivism. It can investigate a range of factors beyond race, such as socioeconomic conditions, education, systemic inequalities, and more.

By identifying the root causes and causal relationships, causal AI can provide better explanations and insights, allowing for fairer and more accurate decision-making in the criminal justice system.

Treating Cardiac Diseases

AI models consume data and derive patterns, which may suggest that individuals from certain races are more susceptible to heart conditions. While this information may be relevant for academic purposes, it is crucial not to treat it as a basis for healthcare decisions or patient treatment.

Relying solely on AI predictions can be dangerous, especially in the medical field. Treating patients solely based on their racial or community background, as indicated by AI models, can lead to disastrous consequences.

Causal AI, however, offers a more comprehensive approach by examining individual cases and understanding the causal relationships between factors. It considers multiple cases, their causes, and their effects to establish a solid model that provides a deeper understanding of the issue at hand.

The Bottom Line

Does AI lose its significance with the emergence of Causal AI? No, because both work together to provide better support for humans.

AI’s predictive intelligence remains valuable in certain domains, such as agriculture, where it can utilize satellite data and other sources to analyze patterns of pest attacks on crops and recommend effective remedies. However, in areas like finance, policy decisions, and healthcare, predictive intelligence alone may not suffice.

Causal AI, on the other hand, examines the cause-and-effect relationships within the patterns and data generated by AI, enabling more robust analyses. Causal AI relies on the data and patterns created by AI to analyze cause and effect, leading to enhanced insights and analysis.

Therefore, AI and causal AI complement each other, allowing for a more comprehensive understanding of complex systems and improving decision-making processes.

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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…