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Decoding AI Accuracy: Unraveling Biases, Complexities, and Limitations

While AI systems are celebrated for their impressive performance, we will delve into the inherent limitations and biases that challenge the notion that accuracy is a single, fixed number. We shed light on the sometimes misleading nature of accuracy and explore other factors to consider beyond accuracy.


Artificial Intelligence (AI) has become an integral part of our lives, shaping the way we interact with technology and information. As AI systems have evolved, the pursuit of accuracy has been a focal point in their development. However, the conventional perception of accuracy as a singular, definitive number needs to be challenged.


In this blog, we will decode the fallacy of AI accuracy and explore why it can be subjective and sometimes misleading. We will then discuss the multi-dimensionality and complexity of correctness, emphasizing the importance of considering other factors beyond accuracy when looking at AI solutions. We’ll also explore DigitalOwl’s approach to accuracy as well as how we strive to overcome the complexities of AI.


The Limitations of AI Algorithms


AI accuracy refers to the ability of AI systems to make correct predictions or decisions. While accuracy is a fundamental metric for evaluating AI performance, it is essential to approach it with caution. Many assume that an AI with high accuracy equates to infallible predictions, but this overlooks the underlying complexities and uncertainties involved in AI algorithms.


The notion that accuracy is a single, fixed number is a common misconception. In reality, it is subjective and context-dependent. AI accuracy is measured by comparing predictions to ground-truth data, but the selection of data used for this evaluation can influence the perceived accuracy. The choice of training data, validation sets, and testing samples can significantly impact the results, leading to biased assessments.


Consequently, measuring the accuracy of an AI based solely on a numerical value cannot provide a comprehensive or definitive assessment of an AI system's performance. This is because accuracy, when presented as a single number, tends to oversimplify the complexity and multifaceted nature of AI's behavior in real-world scenarios.


An AI system's performance hinges on a multitude of factors, ranging from the quality and representativeness of the training data to the robustness of its algorithms in handling diverse and novel situations. Relying solely on an accuracy score disregards the intricate interplay of these factors and the potential biases present in the data, which can significantly influence the system's predictions.


Additionally, contextual factors such as interpretability, adaptability, and the presence of biases must be taken into account. These factors play a significant role in determining an AI system's effectiveness and potential risks.


The Misleading Nature of Accuracy


Focusing solely on accuracy can be misleading, as it does not account for false positives and false negatives. Some AI platforms boast about being 99.9%* accurate, creating an impression of near-perfection. However, it's essential to recognize that this number requires careful scrutiny. For example, if you construct an “HIV test" that returns false automatically, without considering the sample at hand, it will have a 99.3% accuracy as the HIV prevalence is 0.7%. Hence we constructed additional performance metrics such as precision and recall.


This example underscores that accuracy alone isn't a definitive measure of an AI system's effectiveness. It's a nuanced metric that needs to be considered alongside other performance indicators to provide a comprehensive evaluation.


The misleading aspect of focusing solely on accuracy lies in the assumption that all misclassifications bear equal weight, disregarding the varying degrees of severity for different misclassifications. In reality, different types of errors have different consequences. Missing a cancer diagnosis, for instance, carries far more significant implications than an AI overlooking mild headaches.


Accuracy, however, is still an important metric for any AI — especially those used in the medical and insurance industries. But instead of quantifying accuracy down to a simple number, we should embrace a more holistic approach that takes into account the multifaceted nature of correctness and the varying impacts of different types of errors. Understanding these distinctions is pivotal in grasping the true efficacy of AI systems and making informed decisions based on their interpretations.


The Multi-Dimensionality and Complexity of Correctness


A multi-dimensional approach to correctness underscores the intricate nature of AI systems. It emphasizes that assessing AI performance involves understanding the significance of each dimension and the varying degrees of errors within them. As we navigate through the complexities of AI accuracy, we realize the importance of comprehensive evaluations that consider these nuanced aspects, leading us to more accurate and reliable results.


Extracting information from data sources involves multiple dimensions, each contributing to its overall correctness. These dimensions encompass various aspects, including:


  • The medical diagnosis or event

  • Its date

  • Its associated report

  • The reporting provider

  • The institute it was carried out

  • If it's a diagnosis procedure or lab test, its value, and units

  • Enriched information associated with that extraction, such as severity, body part, associated umbrella impairment, etc.


Even when focusing on a single dimension, correctness is seldom a binary concept of right or wrong. It often exists on a continuum, where the degree of accuracy varies. For instance, extracting a patient's name inaccurately as the provider holds a different significance than getting the provider's name correct but with a minor letter discrepancy due to OCR errors. Similarly, extracting "fracture" instead of the specific term "open fracture" constitutes a different level of error than missing the information entirely or inaccurately extracting "migraine."


Considering Other Factors Beyond Accuracy


To truly assess the value and reliability of an AI system, various factors must be considered alongside accuracy. Interpretability is one such factor, which allows users to understand how the AI arrives at its conclusions. AIs that don’t allow users to see how it arrives at a conclusion, or links back to its source materials to show where it got its information, are referred to as “black-box” AIs. A transparent AI system will show how it reached a certain conclusion, which helps to build trust and fosters a better human-AI partnership.


The basic premise in assessing the model quality of an AI is that you can compare its result to some “ground truth”. Some gold standard in which an agreement reflects a correct model result and a disagreement reflects a model mistake. Normally, humans are used to generate those gold standards results. We can compare our model to it.


But what happens when the model performs better than the average human? To what can we compare its result to? How do you deal with situations where there is a significant level of inconsistency between human taggers for the same task? We are getting to the point where it becomes harder and harder to assess our models because the tasks become more complex, the answers become less straightforward and easy to agree on, and the models perform with superhuman capabilities.


The Complications of Generative AI


While entity recognition models extract different medical entities, generative AI models produce a human-like text from scratch. Most of us familiar with ChatGPT know that it’s able to generate complete paragraphs as a response to a question or instruction. While measuring the performance of the entity recognition is complicated, generative texts are much more complicated to assess. The same question can be answered multiple different ways: all written fundamentally different yet all are correct. Assessing the quality of the output is a completely different game and deserves its own article.


One approach to overcome the difficulties mentioned above is to go “one level up” and focus on the core value of the AI. Instead of having a human assess the quality of the generative AI, measure how much engagement it produces in the wild. Instead of aligning the medical extractions to some human-produced group truth, compare the underwriting score a professional arrived at based on a model summary vs a human summary. Focusing on those real “cash value” solves, many of the technical issues we mentioned are, in fact, the true goal of reliable AI systems. However, performing them is often a labor-intensive and difficult project.


Our Approach to AI Accuracy


At DigitalOwl, we understand the complexities and challenges of assessing AI correctness. By offering a holistic approach to AI evaluation, we ensure peace of mind for our users. In order to create an AI that’s truly reliable, we employ multiple data sets, including our proprietary generative AI, comprehensive medical knowledge base, and our entity recognition model, to provide a comprehensive evaluation of AI system performance.


Our AI also prioritizes interpretability, allowing users to understand the AI system's decision-making process by linking back to the source documents and data in order to show where it got its information. Unlike other black-box AI systems, ours always provides insights into the model's inner workings. This allows users to identify potential biases, rectify errors, and build trust with the AI.


Our AI models undergo rigorous stress tests, evaluating their performance under challenging conditions and unexpected inputs. This helps DigitalOwl's AI systems perform consistently, even in real-world situations where the data may deviate from the training set.


Conclusion


AI accuracy as a single, definitive number is a fallacy that needs to be challenged. By acknowledging the subjectivity and limitations of accuracy measurements, we can embrace a more realistic approach to AI correctness. Understanding the multi-dimensionality and complexity of correctness helps us appreciate the significance of factors beyond accuracy, such as interpretability and robustness.


DigitalOwl exemplifies how AI providers can address these challenges and instill peace of mind in their users. By offering comprehensive evaluation metrics, promoting interpretability, and emphasizing coherence and coverage, DigitalOwl sets a benchmark for AI systems that prioritize accuracy. As AI continues to evolve and shape the world, adopting responsible and forward-thinking practices will be essential to ensuring a positive and sustainable future.


Contact DigitalOwl to answer any questions you may have about our generative AI, and be sure to set up a demo with us today!


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