Explainable Machine Learning (ML), sometimes referred to as "XAI" or Explainable AI, is an essential paradigm in the continuously evolving field of artificial intelligence. The underlying concept is to create machine learning models whose processes and predictions are transparent, comprehensible, and capable of being interpreted and justified by human users.

• Interpretability: Explainable ML models are inherently interpretable, meaning users are able to understand the relationship between input data and the predicted outcome.
• Transparency: With transparency, a clear understanding of how the model works, what its parameters mean, and how changes in input data affect the predictions generated is attainable.
• Predictability: Explainable models provide justification for model predictions, enabling a clear understanding on how and why a particular prediction was made.
• Fidelity: In explainable models, fidelity concerns the extent to which the model’s explanation accurately represents its inner workings.
• Accountability: Explainable machine learning also boasts a strong level of accountability as model developers and users are held responsible for the outcomes of their models.

Explainable Machine Learning is widely adopted across diverse sectors due to its prospective benefits, including better decision-making, increased trust in machine learning systems, regulatory compliance, and enhanced model debugging and validation.

The successful implementation of Explainable Machine Learning involves a carefully considered process. Organizations need to understand their requirements and constraints concerning explainability and interpretability. Selecting models that align with these needs is crucial. Once an appropriate model is chosen, the process moves on to a cost-benefit analysis, a seemingly complex task given the balance required between the enhanced interpretability of an explainable model and possible sacrifices in performance.

Furthermore, organizations must ensure all stakeholders, including decision-makers and end-users, are informed about the limits and benefits of Explainable Machine Learning models to ensure realistic expectations and optimized utilization.

Explainable ML is preferred for various reasons, such as:

• Increased transparency: Unlike conventional machine learning models, which are frequently described as 'black boxes,' explainable ML models provide a clear understanding of how decisions are made. This transparency fosters trust and confidence in the machine learning model.
• Enhanced regulation compliance: With an increased focus on data rights and privacy regulations, like GDPR, explainable machine learning aids organizations in fulfilling the obligation to explain, in comprehensible terms, how automatic decisions were made.
• Improved decision-making: By providing clear insight into how it analyses data to make predictions, explainable machine learning allows users to make more informed decisions.
• Extended debugging and validation: The transparency of explainable models gives data scientists the ability to look under the hood, identify problems and biases, and validate their models with an enhanced level of precision.

Despite several advantages, some potential drawbacks of using Explainable Machine Learning are:

• Inherent complexity: Machine learning models can be highly complex, making their interpretation and explanation challenging at times.
• Potential for misuse: Greater transparency can inadvertently expose sensitive model features which could be potentially exploited.
• Trade-off between performance and interpretability: Higher levels of interpretability may require simplified models, which might slightly compromise the performance of the model.

In summary, Explainable Machine Learning presents a promising path towards solving the black box issue present in many machine learning models. The ability to understand, interpret, and justify model predictions significantly enhances trust in these systems and drives better-informed decision-making. However, it calls for organizations to critically evaluate their needs and potential trade-offs before jumping into implementation.