¿Cómo se aumenta el valor de la IA explicable?

It becomes very important for the AI teams to understand the end-users’ experience and interface with the AI product. Once these insights are considered while designing the model and considering the datasets - testing, validation and training; it would ensure that the output of the model are more explainable and are a step forward to design and build AI more responsibly.

With all of the hype around AI at the moment, an AI Engineer also needs to compete with misinformation among their audience. It can be helpful to understand that while your audience (as well as yourself) may think that the model is understood, it may not be the case. AI is filled with probabilities and uncertainty, even when results seem like sure things. It is okay to not know 100% the reasoning behind it. This just needs to be made clear to your audience, and you will build trust in the process.

Exactly, know your audience and who is stake holders . Your business team wants to know the flow of application process while Data scientist & ML engineer wants to know how AI makes decisions while using XAI.

The first step to increase the value of explainable AI is to understand your audience and tailor explanations to their needs. Technical experts may want algorithm and data details, while general users focus on outcomes and impacts. Explanations should provide the right information and depth for the stakeholder. For example, engineers may get implementation specifics, executives get business insights, and regulators get ethical factors. Layered explanations that balance overview and nuanced details can build understanding across diverse audiences. Meet stakeholders where they are - explain benefits first before limitations, staying solution-focused.

People tend to fall into 3 categories of learning: 1. Learning by seeing, 2. Learning by hearing, and 3. Learning by doing. Tailor your format to this.

Strive for clear, concise explanations. Avoid jargon. Signpost key takeaways. Explainability's value multiplies when insights feel accessible and digestible. The right formats turn complexity into understanding. Text and images work for reports. Charts and graphs for analysis. Video and interactives aid exploration. Tailor language as well. Plain terms suit users. Technical details help experts. Analogies and examples engage broader groups. Layer modes - combine text, visuals, metaphors. Reinforce ideas across mediums.

The key here is to ensure that the explanations are not only correct but also relatable. Just like a good story is something that's clear, concise and can resonate with your audience. Similarly, making AI explanations relatable and interesting will increase its value by 10x!

High quality explanations build trust and adoption. Assess rigorously and improve continuously. Here's the checklist I use: 1. Targeting - Are they adapted to the stakeholder digesting it? 2. Accuracy - Are explanations factually correct? 3. Consistency - Do they align with model behavior? 4. Completeness - Do they provide sufficient context? 5. Fairness - Do they avoid biases?

By incorporating XAI in your ML development cycle, businesses can increase the value of AI systems to make them more trustworthy and transparent. This is because XAI can help us understand why an AI system made a certain decision, and also using counterfactuals can show us what would have happened if different model inputs had been given. For e.g., XAI could help a loan applicant understand why their loan got rejected, and it could also show how to improve their chances of getting a loan in the future. Thus, XAI can help us to identify and mitigate any biases that may be present in the models, build trust between businesses and customers, thereby help ensure that AI systems are used fairly and ethically.

Knowingly or unknowingly, look and feel is the first thing that we humans base our judgement on. So, if you want others to drive value from something they first need to be attracted and then engaged to it. Good design and better user experience is the only way to achieve that!

It's an ongoing journey, not a destination. Applying the Kaizen methodology can be a useful angle - it is based on continuous incremental improvements over time. Similarly, enhancing explainable AI is an iterative process of small enhancements that compound. - Simplifying algorithms is eliminating inefficiencies. - Adding tracing is like monitoring systems - it provides visibility. - Refining documentation is standardization - clearer, consistent communication. - Testing edge cases mirrors gathering user feedback uncovers issues. - Each iteration builds on the last - compounding gains in quality and understanding. PS: Kaizen comes from the Toyota Production System (TPS) and the lean manufacturing movement.

Explainability has limitations we must acknowledge and address: Complexity - Simplify locally to aid understanding Uncertainty - Quantify confidence, don't overclaim Trade-offs - Balance explainability with accuracy, privacy, fairness Ethics - Ensure explanations are unbiased, respect rights and values Set expectations on what can and can't be explained. Be transparent about risks.

Publicly available data sets for benchmark might be also useful, such that users can understand how a model performs on a pre-defined universe, including corner case and problematic issues. This would not provide explainable AI in itself, but it could be another useful ingredient in such a complex scenario.

Explainable AI is a active field of research, which incredibly challenging topics in the table. Some models might allow an analytic explainer, while others would maybe only allow behavioral explanations. If you want, think of a toy experiment with a linear regression… writing an explainer by exposing parameters might be easy, but it should be carefully complemented with examples and corner cases. Then, think of a non-linear regression: is it important to know the level of fitting of the model together with parameters? Maybe yes.

Explanations act as a bridge between the human and machine worlds. With explainable AI, the goal is to provide transparency on how decisions are made by AI systems and help build trust between humans and machines. Ultimately, explainable AI systems earn trust by taking a human-centric approach. They empower users with knowledge, not confuse them with opacity. Understanding ushers trust. Explaining uncertainties and limitations sets proper expectations. Trust emerges when systems don't overclaim. Personalized explanations that resonate with users show care and emotional intelligence. This elevates AI as collaborators, not just tools.