There is no shortage of challenges accompanied by the use of AI, however, for every problem encountered, there is a way to mitigate it. For the more technical portion of the challenges in AI, there are two approaches that can be used to deliver a meaningful explanation: model-agnostic approaches and model-specific approaches.

  

Model agnostic approaches can be applied to any model (entire groups of learning techniques or algorithms). Essentially, the model is treated as a “black box”, meaning that this method does not require looking into the complex inner processes of the model. Model agnostic techniques include the use of evaluation metrics; precision, recall, and MSE (mean squared error). These methods allow for variability with model selection because there is no reason to develop different evaluation method structures for different model types.

 

It also provides a standard “metric system” allowing for the comparison of model comparison, using the same system of measurements. This technique does not utilize the structure of the model and derives an explanation by discomposing and transforming input data and evaluating the performance of the transformed data with respect to the original data’s performance.  

 

Model-specific approaches are only applied to specific types of models or specific techniques. In this method, the model is treated as a “white box”, referring to the approach in which testers inspect and analyze the inner workings of a given software system. It is based on the details of the particular structures of the applied machine/deep learning model used; this technique is strictly applied to specific model architecture. The model-specific approach essentially delves into the interior of any given machine learning model, like the neural network. Then, the reverse-engineering approach is applied to explain how deep learning or machine learning is producing an outcome. This provides a higher quality explanation and understanding of a decision because it probes the internal workings of the model.

 

The only downside of this technique is that it requires needing to sift through the entire model structure, potentially compromising performance. In both model-specific and model agnostic approaches, the local method refers to the interpretation of specific, individual data points, while the global method refers to interpreting common patterns across a multitude of data points.  

 

Beyond simply technical problems, XAI presents problems with transparency and legal issues. For one, currently, XAI techniques are developed by machine-learning engineers meaning that their needs are incorporated and prioritized more than the common user. To avoid this occurrence, XAI’s general objectives should be diversified; it is critical to recognize the needs of users, stakeholders, and impacted communities and increase awareness and motive to accomplish said objectives. Critical perspectives to consider in terms of user explanation needs include explaining the context and reasoning behind an explanation, communication about unreliability within model predictions, and facilitating some sort of interaction with the explanation. 

 

Furthermore, some sort of XAI metrics must be established to create a somewhat uniform set of expectations for the delivered explanation. For one, the ideal of a “good explanation” should be evaluated, XAI approaches and the different ways transparency is lacking should be analyzed, and a standardized set of metrics must be developed. Finally, there must be a system in place to mitigate the risks within explainability.

 

Explainability can be unpredictable, providing deceptive or misleading explanations, and creating security risks, because they contain sensitive information about the model and its performance. This sensitive information can be used to replicate a model and can be utilized by competitors. It is critical to establish methods to document and avoid these risks and to successfully do so, establish standards and policy guidelines with reasonable measures.  

What is ANAI and how can we help?

While XAI comes with a fair number of risks, developers are exploring ways to ensure transparency and security to provide impacted communities with trusted explainability.

ANAI is an end-to-end machine learning platform to build, deploy and manage AI models at a faster rate saving a ton of time and money spent on building AI-based systems. It enables entities to handle and process data, create exploratory and insightful visuals, and make the data ML ready.

ANAI’s AutoML pipeline utilizes the transformed data and extracts the correct features from it so that the model learns the most important details from the data. The data is then passed to the ML pipeline where various ML models are trained and only the best out of them are selected for deployment. ANAI’s MLOps allows users to keep a tab on their models even after deployment to check for model drifts and performance issues.

But due to all this automation of AI, there’s always a chance of untrust regarding the model results and as AI models are already termed black boxes because they provide no insights within their functioning, it again becomes more difficult. To solve this ANAI also has a model explanation pipeline called Explainable AI (XAI) that generates explanations of the model’s results allowing us to look behind the curtains, remove biases and other inconsistencies, and ultimately create a trustable, fair, and responsible AI system.

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