Explaination-Providing AI (XAI) System

A Explaination-Providing AI (XAI) System is an AI system that provides understandable reasons behind its decisions and actions, making its processes transparent and comprehensible to humans.

• Context:
  • It can be a transparent machine learning system or an AI system with clearly defined decision rules.
  • It can solve an Explainable Artificial Intelligence (XAI) Task by implementing an Explainable Artificial Intelligence (XAI) Algorithm.
  • It aims to improve trust and accountability by allowing users to understand how and why decisions are made.
  • It helps ensure that AI systems comply with legal and ethical standards, such as the Right-to-Explanation.
  • ...
• Example(s):
  • An AI model highlighting which features were most influential in making a medical diagnosis.
  • A recommendation system that provides reasons for suggesting certain products or content to users.
  • An autonomous driving system that explains its actions in critical situations to enhance safety and trust.
  • ...
• Counter-Example(s):
  • Black Box AI: An AI system whose internal workings and decision-making processes are not transparent or understandable, even to its designers.
• See: Artificial Intelligence, Explanation, Right-to-Explanation, User Experience, White-Box Testing, Prediction Explainability, Interpretable Machine Learning, Transparency in AI, Algorithmic Transparency, Accumulated Local Effects (ALE), AGI Controllability.

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References

2024

• Perplexity: Interpretability vs. Explainability:
  • Interpretability refers to the ability to understand the internal mechanics or logic of how a machine learning model works and makes predictions. An interpretable model is one where a human can comprehend the relationship between the input features and the output, as well as the relative importance or weightings assigned to each feature in the decision-making process.
    • Some examples of interpretable models include linear regression, logistic regression, and decision trees. These models have an inherent structure or form that makes it easier for humans to reason about how they arrive at their predictions. For instance, in a linear regression model, the coefficients directly represent the change in the output for a one-unit change in the corresponding input feature, all else being equal.
  • Explainability, on the other hand, focuses on providing explanations for the specific predictions or outputs made by a model, even if the internal workings of the model itself are opaque or difficult to interpret directly. The goal of explainable AI (XAI) technique]]s is to open up the "black box" of complex models like deep neural networks and generate human-understandable reasons for individual decisions.
    • Explainability methods like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) work by approximating the behavior of the complex model locally around a specific prediction, using interpretable surrogate models or feature importance measures. These methods aim to explain why a particular input instance received the output it did, rather than fully elucidating the global logic of the entire model.
  • While interpretability is about understanding the AI model itself, decision explainability focuses on understanding the AI model's decisions. Interpretability is about understanding the AI model itself and how it transforms inputs to outputs in a way that humans can grasp intuitively. AI decision explainability, on the other hand, focuses on providing post-hoc explanations for individual predictions made by the model, even if the model's internal mechanics are complex or opaque.
  • Citations:

[1] https://datascience.aero/explainability-interpretability-what-model-need/
[2] https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html
[3] https://datascience.stackexchange.com/questions/99808/an-example-of-explainable-but-not-interpretable-ml-model
[4] https://blogs.sas.com/content/hiddeninsights/2022/08/10/interpretability-vs-explainability-the-black-box-of-machine-learning/
[5] https://christophm.github.io/interpretable-ml-book/
[6] https://link.springer.com/chapter/10.1007/978-3-031-04083-2_2
[7] https://docs.aws.amazon.com/whitepapers/latest/model-explainability-aws-ai-ml/interpretability-versus-explainability.html
[8] https://www.ibm.com/topics/explainable-ai
[9] https://quiq.com/blog/explainability-vs-interpretability/
[10] https://www.datacamp.com/tutorial/explainable-ai-understanding-and-trusting-machine-learning-models 
[11] https://datascience.stackexchange.com/questions/70164/what-is-the-difference-between-explainable-and-interpretable-machine-learning

2024

• (Yampolskiy, 2024) ⇒ Roman Yampolskiy. (2024). “AI: Unexplainable, Unpredictable, Uncontrollable.” CRC Press. ISBN:9781032576268
  • NOTE: It delves into the challenges of AGI explainability and AGI incomprehensibility in AI. It discusses the difficulty of providing accurate and understandable explanations for AI decisions, especially those made by complex models like deep neural networks. It also explores the concept of incomprehensibility, which refers to the limitations of human understanding in fully grasping the complex reasoning behind AGI actions. It highlights the implications of these challenges for AGI safety and AGI security.

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Explainable_artificial_intelligence Retrieved:2023-8-9.
  • Explainable AI (XAI), also known as Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the reasoning behind decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.

    XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason. XAI may be an implementation of the social right to explanation. Even if there is no such legal right or regulatory requirement, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. XAI aims to explain what has been done, what is being done, and what will be done next, and to unveil which information these actions are based on. This makes it possible to confirm existing knowledge, challenge existing knowledge, and generate new assumptions.

     Machine learning (ML) algorithms used in AI can be categorized as white-box or black-box. White-box models provide results that are understandable to experts in the domain. Black-box models, on the other hand, are extremely hard to explain and can hardly be understood even by domain experts. XAI algorithms follow the three principles of transparency, interpretability, and explainability. A model is transparent “if the processes that extract model parameters from training data and generate labels from testing data can be described and motivated by the approach designer.” Interpretability describes the possibility of comprehending the ML model and presenting the underlying basis for decision-making in a way that is understandable to humans. Explainability is a concept that is recognized as important, but a consensus definition is not available. One possibility is “the collection of features of the interpretable domain that have contributed, for a given example, to producing a decision (e.g., classification or regression)”. If algorithms fulfill these principles, they provide a basis for justifying decisions, tracking them and thereby verifying them, improving the algorithms, and exploring new facts.

    Sometimes it is also possible to achieve a high-accuracy result with a white-box ML algorithm that is interpretable . This is especially important in domains like medicine, defense, finance, and law, where it is crucial to understand decisions and build trust in the algorithms. Many researchers argue that, at least for supervised machine learning, the way forward is symbolic regression, where the algorithm searches the space of mathematical expressions to find the model that best fits a given dataset.

    AI systems optimize behavior to satisfy a mathematically specified goal system chosen by the system designers, such as the command "maximize accuracy of assessing how positive film reviews are in the test dataset." The AI may learn useful general rules from the test set, such as "reviews containing the word "horrible" are likely to be negative." However, it may also learn inappropriate rules, such as "reviews containing 'Daniel Day-Lewis' are usually positive"; such rules may be undesirable if they are likely to fail to generalize outside the training set, or if people consider the rule to be "cheating" or "unfair." A human can audit rules in an XAI to get an idea of how likely the system is to generalize to future real-world data outside the test set.

2020

• (Treviso & Martins, 2020) ⇒ Marcos V Treviso, and André F.T. Martins. (2020). “The Explanation Game: Towards Prediction Explainability through Sparse Communication.” In: arXiv preprint arXiv:2004.13876. doi:10.48550/arXiv.2004.13876
  • ABSTRACT: ... ...

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