How do you select the right tools for explainable AI?

Global explanations in Explainable AI (XAI) give an overarching view of a model's behavior, showcasing consistent decision influencers across various inputs. In contrast, local explanations delve into specific decisions, revealing why a model arrived at a particular outcome for a given instance. For organizations, global explanations help in understanding the model's general tendencies, aiding in broad adjustments and policy-making. Local explanations, on the other hand, are crucial when justifying individual decisions to stakeholders or spotting anomalies. Together, they ensure a model's transparency and bolster trust in its decisions, aligning AI processes with organizational goals.

Overall, XAI must strive to be as transparently explainable as possible. Otherwise it runs the risk of falling short, and hence lacking confidence in its overall performance.

As a non-technical person, I prefer code-light tools. For example, I nearly gave up on Midjourney due to the complexity of the user interface. Most LLMs feel more accessible because you get what you need with little specialized expertise required.

Here are a few key factors to consider when selecting tools for explainable AI Model transparency Model interpretation Explanations for specific users Explainability across ML lifecycle Algorithm agnostic Model debugging Model monitoring Easy integration Visualizations The key is to test out shortlisted tools on your own models/data to see how well they meet your explainability needs. The right XAI tools can increase trust and value in AI systems.

Before using complex models, we need to ask ourselves if these models are needed and whether the problem at hand could be solved by less complex models. The other question is what is the impact of the AI model? If we are using AI to recommend movies or places to visit the impacts are very different from using AI to diagnose illness such as cancer. The higher the impact the more likely is the need for XAI.

Explainable, transparent, and ethical AI is crucial for responsible AI adoption. Explainability ensures users understand AI decisions, fostering trust. Transparency demystifies the AI 'black box', facilitating audits and improvements. Ethical AI, by upholding fairness, prevents reinforcing societal biases, ensuring just outcomes. As AI increasingly impacts diverse sectors, its decisions can profoundly shape lives. Without these principles, we risk unintended harms, from amplifying inequalities to undermining public confidence in technology. Thus, ensuring AI is explainable, transparent, and ethical is not just good practice—it's essential for its sustainable and beneficial integration into society.

Everything about any and every AI model should be fully and transparently explainable. You should strive to OVER-explain every aspect, so that no questions arise. Then it will be genuine XAI.

Without any specific, named instances of AI softwares or applications as well as their pros and cons, we barely can find a way to apply AI in our fields!