How can you explain AI model decisions to non-experts?

Es ist allerdings nicht klar, ob man maschinelle Entscheidungen wirklich erkl?ren kann. Maschinen lernen anders als Menschen und machen daher auch andere Arten von Fehlern. Gleichzeitig k?nnen sie die Muster, die sie in Trainingsdaten gefunden haben, nicht in menschlichen Worten erkl?ren. Auch sogenannte xAI (explainable AI)-Methoden k?nnen das nicht. Daher w?re Erkl?rbarkeit zwar wichtig, kann aber mit heutigen Methoden des maschinellen Lernens nicht erreicht werden (s. mein Buch "Die KI war's").

Explainability fosters trust and transparency by providing insights into the decision-making process, especially in complex models like neural networks. This is essential for stakeholders, policymakers, and end-users to comprehend the rationale behind AI predictions. Employing interpretable models or post-hoc explanation techniques, such as LIME or SHAP, enhances explainability. Collaborate with experts to strike a balance between model complexity and interpretability, tailoring explanations to the audience's level of understanding. Recognizing the significance of explainability not only ensures informed decision-making but also promotes the responsible and ethical deployment of AI technologies in diverse applications.

In my experience, Model explainability plays a very critical role not only from data scientist perspective but also from business perspective, a black box model doesn't give us intuition on how it takes decision, a interpretable model help in tuning the model and move closer towards achieving more accurate and transparent prediction Regulation also require Responsible AI in area like Finance - Loan, Medical - Disease prediction, Insurance product so that the end user in not adversely impacted by the outcome of the model, It is data scientist responsibility to have model explainability designed and MLOps team responsibility to maintain this in production for key stakeholders

Explaining AI decisions to non-experts is like simplifying a complex recipe. Focus on the essentials: Purpose: Clarify what the AI does (e.g., loan approval, disease diagnosis). Data Inputs: Describe the information the AI uses, like ingredients in a recipe. Decision Process: Explain in simple terms how the AI weighs different factors, akin to a teacher grading a test – some answers (factors) count more than others.

The increasing integration of AI technology in diverse sectors underscores the critical importance of explainability. While machine-learning models excel in discerning patterns, the collaboration between AI and human decision-making introduces a potential trade-off: a lack of transparency in AI's decision processes. This becomes particularly concerning in scenarios such as college admissions, workplace safety, medical diagnoses, or business predictions. In these instances, the need for clear explanations is evident. Individuals facing adverse outcomes seek to comprehend the precise methods used by machine-learning systems.

A good explanation should be clear, concise, and tailored to the audience's level of understanding, avoiding unnecessary technical jargon. It must highlight relevant features and factors influencing the model's output, providing a comprehensible narrative. Leveraging visual aids, such as feature importance plots, enhances clarity. Employing interpretable models or post-hoc techniques like LIME ensures transparency. Collaborate with experts to strike a balance between simplicity and informativeness, fostering a meaningful understanding of AI decisions. Recognizing the criteria for a good explanation is instrumental in bridging the communication gap between AI practitioners and non-expert stakeholders.

A good explanation of an AI model should be relevant, simple, trustworthy, and enable the non-expert to make informed decisions. An example of a successful project that achieved this is the Explainable AI (XAI) project by the Defense Advanced Research Projects Agency (DARPA). The project aimed to develop AI systems that could explain their decision-making processes in a way that is understandable to human users, particularly in high-stakes situations such as military operations. The XAI project successfully created AI models that could provide explanations for their decisions in natural language, allowing human users to understand the reasoning behind the AI's actions. This enabled military personnel to trust and rely on the AI systems.

A good explanation for an AI model is not just a technical description; it's a communication tool that helps non-experts understand the model's objectives, assumptions, limitations, and uncertainties. A quality explanation should: Relevance: Focus on the aspects of the model that are most important to the non-expert. Simplicity: Use plain language, avoid jargon, and include concrete examples or analogies for clarity. Trustworthiness: Be accurate, consistent, and honest about any model errors, biases, or uncertainties. Empowerment: Enable non-experts to make informed decisions, provide feedback, or request additional information.

Easy: it needs to have predictive power and to be short, as short as possible (like this explanation :-), at the limit of characters)

If the non-expert can take a model's explanation and then draw inferences on how certain variables would impact model output, that is clear validation of a high quality explanation. The non-expert has internalized the understanding so well that they can explain the ins and outs of the model, even if they don't focus much on the mechanics of model functionality.

Employing interpretable models or post-hoc techniques, such as LIME or SHAP, facilitates the generation of accessible explanations. These methods distill complex model outputs into understandable insights, shedding light on the key features influencing predictions. Collaborate with experts to customize explanation strategies, ensuring alignment with the audience's familiarity with technical concepts. Utilizing visual aids and simplified narratives enhances the clarity of explanations. Recognizing the nuanced process of explanation generation empowers AI practitioners to bridge the gap between technical intricacies and non-expert comprehension, fostering informed decision-making.

To generate explanations for AI model decisions, various methods and tools can be employed, depending on the model's complexity, data, and context: Feature Importance: This method reveals the contribution of each input feature to the model's output prediction. Partial Dependence Plots: These plots illustrate how the model's prediction changes as a single input feature varies, helping understand feature interactions. Counterfactual Examples: Display what input values would need to change to produce a different output prediction, providing insight into model behavior. Decision Rules: These demonstrate the logical rules used by the model to make predictions based on input values.

Another possibility is to compute the SHAP values of your model. Individually, they provide explanations for each variable contribution for a given prediction. Together, they can be used to compute some the cumulative feature contributions, and in my experience, very often this provide better results than the classic feature importance.

Es ist durchaus kontrovers, wieviel Erkl?rungskraft die genannten Methoden besitzen. LIME ist beispielsweise nur dafür geeignet, die Ergebnisse vorherzusagen, die eine Maschine für ?hnlich gelagerten Input gegeben h?tte. Das hilft nicht viel, wenn man verstehen will, ob die Maschine beispielsweise diskriminierend entscheidet.

You can use model agnostic techniques such as Shapley values or LIME or you can have two model environments, one for prediction and an explainable model for explainability. Also recently some NN explainable techniques are being researched.

Considerations in this process include: 1. Transparency: To achieve this, we must consider the model's simulatability, decomposability, and algorithmic transparency. Simulatability refers to how easy it is for a human to simulate the model's decision-making process. Decomposability involves breaking down the model into understandable components, while algorithmic transparency means understanding the procedures used by the model to generate its outputs. 2. Clear Visual References: Various post-hoc explanations can be utilized, such as textual or visual explanations, or local explanations focused on specific instances or areas of the model's decision-making process.

Explanations must be divided into different slides where each slide should be understood by a non-technical person in less than 3 minutes. Speech must be fluent and efective. Use also lots of images and graphics, you must give the audience the impression that a technical topic is easy to be understood.

Speak their language! Metaphors & use cases can simplify concepts, helping your audience make connections. For example, use a metaphor like Baking Cookies to describe what ingredients (e.g., input data) and step-by-step instructions (e.g., algorithm rules) are used to create the output (e.g., recommendation or answer). You can illustrate the impact missing data or modifications to the process instructions can have in creating totally different outputs. Ask your non-technical partners to list examples where the ML output is undesirable. Then, walk backward through the instructions & inputs used to identify how to improve the output. You might find this common language or framework creates a deeper understanding for both parties.

A good way of explaining trade-offs in decision-making by ML models to non-technical stakeholders is by focusing on the types of errors the model will make: “Would you rather avoid this type of error occurring or that type of error, or are they equally important to reduce?”. In a content moderation team, the following conversation could begin with a data scientist asking: “Would you rather take down an image because our AI thought that it had harmful content in it but in reality, it didn’t (false positives), or allow an image on our platform that had harmful content in it but it slipped under our radar (false negatives); or are both types of errors equally important to reduce?”.

You can create a simple NLP model that generates the reason behind the model's decisions. One neat trick is to give the saliency map of the input to this explainer model as it's input alongside the output of you main model. This way, the NLP model can express the source of the main model's decision.

Evaluating explanations in the context of AI model decisions involves assessing their fidelity, clarity, and user understanding. Fidelity, I mean, how well the explanation aligns with the model's actual decision-making process. Clarity gauges how easily non-experts can comprehend the explanation. User understanding refers to whether the explanation achieves its intended purpose of making the decision rationale accessible. Additionally, comparing explanations against expert knowledge and refining based on iterative feedback enhances the overall quality of explanations. The goal is to ensure that the provided information is accurate, understandable, and valuable to non-experts.

Understanding and trusting AI model decisions can be challenging, especially when they have significant impacts on our lives, such as health, justice, or security. I believe that one way to make artificial intelligence systems more understandable and trustworthy for people who are not experts in technology is to use examples that illustrate how they work and why they make certain choices.

The quality of explanations can be assessed using metrics like fidelity (how well the explanation reflects the model's behavior) and comprehensibility (how well non-experts understand it). User feedback and testing help refine explanations to meet the audience's needs.

AI model decisions can be hard to understand for non-experts, but there are some ways to make them more transparent and interpretable. Here are some possible ways: Provide explanations and visualizations that can show how the model works, what features it uses, and what outcomes it predicts. For example, you can use feature importance, partial dependence plots, or SHAP values. Use analogies and examples that can relate the model to something familiar or concrete and illustrate its logic and reasoning. For example, you can use metaphors, stories, or case studies.

Collaboration with domain experts is crucial to tailor explanations to the audience's level of understanding while maintaining the balance between model complexity and interpretability. Recognizing the importance of explainability not only supports informed decision-making but also promotes responsible and ethical AI deployment across diverse applications.

Explainable AI is a heavily researched domain in the industry. It's sort of difficult to explain decisions made by more complex models such as ANN. Though there are libraries which help explain models by numbers and probabilities, they are very useful for simpler models such as random forests, classifications, etc but even they are not that good in explaining complex models.

Try to emphasize with the person that you are talking to, to study their background and to think about what they want. At the end of the day, you need to be a business translator, know how to speak both technically and from a business point of view.

When developing a new machine learning model, it is worth keeping in mind the context of its future use. This includes the interpretability of features and their maintainability. Opting for features that are readily interpretable can enhance maintainability. These will be easier to manage and less prone to breakdowns. An example of less advantageous features in this regard include those based on legacy code, susceptible to seasonality, or from upstream models with unclear semantics. Additionally, interpretable features and models tend to facilitate collaboration. They are more likely to be adopted by other teams within the company or shared with the broader open-source community, as comprehensibility often enhances usability.