Your expertise clashes with the AI model's decision. How do you navigate this conflicting territory?

Navigating a conflict between your expertise and an AI model's decision requires a thoughtful and balanced approach. Start by thoroughly analyzing the AI model's output to understand its reasoning and identify any potential biases or limitations in the data or algorithm. Compare this with your own expertise and knowledge, considering whether the AI's decision aligns with the broader context and objectives. Engage in an open dialogue with stakeholders, explaining your perspective while acknowledging the AI's insights. If necessary, propose adjustments to the model or suggest a hybrid approach that leverages both human expertise and AI-driven analysis.

First, use every method available to you in the AI system to understand the reasoning behind its output. If you are dealing with an LLM, it is highly advisable to be familiar with the mathematics of how they generate output tokens. This enables you to contrast their mathematical calculations with your own reasoning, and explain how these two things can generate different outputs. Second, re-examine your own conclusions and consider that the training data of the AI model may include facts that are additional to your own experience. If you can, get details about the training data. Where did it come from? Who reviewed it?

When your expertise clashes with an AI model’s decision, it’s important not to trust AI blindly. Instead, begin by understanding the reasoning behind the AI’s output. Explainable AI (XAI) can provide transparency into the decision-making process, allowing you to examine inputs, model confidence, and conclusions. At Schneider Electric, we believe in balancing AI’s capabilities with human insight. AI excels at processing data, but humans bring context, intuition, and a broader understanding. The key is to use AI as a tool for informed decisions, where technology and human expertise work together for better outcomes.

Trust in AI is crucial, but it shouldn't be blind. When an AI model's decision conflicts with your expertise, start by examining its reasoning. Many AI systems, especially those designed with explainable AI (XAI) principles, offer transparency. Analyze the data inputs, confidence levels, and rationale to determine if the decision is based on solid evidence or if it overlooks aspects your human insight can address.

Data is essential to any AI model, but an AI model is only as good as the data it was fed. If the data within the model does not represent a diverse variety of populations or perspectives, the resulting conclusions the AI will make will be grounded in its own inherited bias. It is important that AI is therefore a collaborative assistant in decision-making processes alongside a diverse team that can apply critical logic to its conclusions, instead of the end-all, be-all.

One thing I’ve found helpful when working with AI is its ability to detect subtle patterns in data, which allows you to address issues before they escalate. For instance, AI systems can identify early signs of mechanical faults or deviations from normal operations, helping prevent costly breakdowns. Actually, I disagree with the notion that AI can replace human expertise entirely. While AI is great at processing large data sets and making predictions, the final decisions should always consider human insights and experience, especially when navigating complex scenarios.

When facing conflicts between your judgment and an AI model's outputs, it's essential to closely examine the underlying data. Evaluate whether the data used was comprehensive, current, and free from biases that could distort the results. Scrutinizing data quality and relevance can clarify whether the AI's decision-making is sound or if there are legitimate reasons for your concerns.

I prioritize understanding the model’s logic and data. By validating its output against my expertise and identifying biases, I achieve a balanced evaluation that merges human insight with AI for the best results.

🧠 AI Model Limitations 🔍 Key Model Limitations Training data biases Limited context understanding Difficulty with rare scenarios Lack of causal reasoning 5️⃣ Potential for outdated information 💡 Assessing AI Decisions Compare scenario to training data Evaluate model's domain expertise Consider recent real-world changes 🤝 Human-AI Collaboration Use AI as a second opinion Combine AI insights with experience Challenge AI decisions constructively Document when human judgment prevails 🔑 Balancing Act Tips Understand model's strengths & weaknesses Trust your expertise in ambiguous situations Use AI to augment, not replace judgment Regularly update your AI knowledge Your expertise adds the critical human element to decision making.

Wenn Ihr Fachwissen mit einer KI-Entscheidung kollidiert, ist es wichtig, die Einschränkungen des Modells zu verstehen. Überlegen Sie, ob das Modell mit ähnlichen Szenarien trainiert wurde und ob bekannte Schwachstellen existieren. Indem Sie diese Grenzen berücksichtigen, können Sie besser einschätzen, wie viel Vertrauen Sie der KI-Entscheidung im Vergleich zu Ihrem eigenen Urteilsvermögen schenken sollten. Es geht darum, die Fähigkeiten des Modells zu respektieren, aber auch zu wissen, wann man auf sein eigenes Fachwissen vertrauen sollte.

Reconhecer as limitações dos modelos de IA é essencial para uma aplicação mais precisa. Entender se os modelos foram treinados em cenários representativos da realidade atual é crucial. A sugestão de realizar treinamentos contínuos com novos dados pode auxiliar na superação dessas limitações. Integrar a experiência humana na identificação de situações onde o modelo falha pode oferecer oportunidades de melhoria contínua, garantindo que as limitações sejam abordadas e minimizadas ao longo do tempo.

It’s essential to ask: Was the training data representative of the current context? Is the model prone to biases or inaccuracies in certain areas? These questions guide us in determining when to trust AI decisions and when to rely on our own expertise. Balancing AI's capabilities with human judgment means appreciating the model's strengths but also recognizing its blind spots. This dual approach ensures that we can maximize the value AI offers while mitigating risks associated with over-reliance on technology. Understanding AI's limitations empowers us to use it as a tool rather than a replacement for critical thinking and experience.

I believe that the interaction of business knowledge with AI in scenario simulation enhances the process, allowing for the refinement and adjustment of parameters based on practical experiences that data alone cannot capture. Experts can validate results, suggest alternative scenarios, and interpret anomalies more accurately, ensuring that simulations are more realistic and aligned with the business context, leading to more informed and robust decisions.

In cases of conflicting decisions, I use scenario simulation to test the AI’s recommendations against real-world outcomes. By comparing these simulations with my own predictions, we can identify any gaps in the model’s logic. This approach ensures that decisions are robust and well-grounded in both AI insights and practical experience.

While devising an AI bot for our stakeholders to manage their customers and product offerings, we gave them an option to know about the products, clarify any doubts of thier customers. 1. The stakeholders started conversing in portmanteau languages 2. Expected more of structured output basis unstructured input 3. Generated outcome that varied from actual response Our approach across all three are as follows inline: 1. Tested with actual users to see what kind of queries they ask, and analysed the no. of query/lang the bot is used for 2. Enabled plugins and enhaanced prompt engineer for handling inputs 3. Mandatory feedback to be collected from every users basis response evoking EQs 4. Reinforced training of model using humnan responses.

𐅣 Ao invés de criar cenários hipotéticos para testar a IA, pense em “simulate yourself” em diferentes situações. 𐅣 Você estaria agindo de acordo com a decisão da IA em todos os cenários possíveis? 𐅣 Utilize técnicas de role-playing e “assuma o papel da IA”, compreendendo suas motivações e imperfeições. 𐅣 Reveja seus próprios atos e sentimentos como se você fosse a IA. 𐅣 Essa imersão no outro lado do conflito te permitirá enxergar a situação sob uma nova perspectiva, levando a um processo de tomada de decisão mais consciente e empático, transcendendo a mera comparação de resultados. #ai #ia

Recomendo expandir essa abordagem, utilizando simulações variadas para identificar os limites e as capacidades do modelo de IA. A combinação de simulações de IA com insights de profissionais experientes pode criar um ambiente de aprendizado mútuo, permitindo ajustes dinâmicos que melhoram a precisão e relevância das decisões automatizadas. Isso também pode gerar novas oportunidades de inovação.

When facing conflicts between AI decisions and your expertise, adopt a collaborative approach. View it as an opportunity to blend AI's predictive power with your contextual understanding. By integrating insights from both AI and human expertise, you can achieve more nuanced and robust outcomes. This synergy allows you to leverage the strengths of each perspective, leading to well-rounded decisions that consider both data-driven predictions and human judgment.

Consider healthcare, where AI tools assist doctors in diagnosing conditions. When an AI model suggested a particular diagnosis, but doctors disagreed based on their expertise, collaboration was key. Instead of viewing the AI as a competitor, doctors used it as a second opinion. They combined the AI’s data-driven insights with their contextual knowledge of the patient’s history. This approach led to more accurate diagnoses, as it balanced the AI's pattern recognition with human understanding. By collaborating, they achieved a more nuanced and reliable outcome, leveraging both strengths effectively.

The most important thing in such a situation where the domain expertise and AI decisions are opposite, a hybrid approach can be taken up where the school of thought should run with the fact to take the matter as to how the domain expert and AI reached its decision. This path will ensure that a structured understanding of both aspects is gained and both aspects can be combined together with assigning weights. At last, a collaborative decision will ensure both perspectives are taken into account ultimately leading to a better result.

*: When a conflict arises, I openly discuss the reasoning behind both the AI's decision and the expert perspective. This fosters collaboration and helps ensure that the final decision is well-informed. By combining AI capabilities with human insight, I aim to offer balanced and thoughtful responses that respect both the power of data-driven analysis and the irreplaceable value of human expertise.

Every disagreement with an AI model presents an opportunity for continuous learning. Regardless of the outcome, analyze the situation to identify lessons that can improve both the AI model and your expertise. This process of reflection and adjustment helps ensure that both you and the AI evolve, leading to more effective collaboration and better decision-making over time. Continuous learning fosters growth and adaptation, enhancing the overall performance of both human and AI systems.

If you can't understand how the AI model predicts it's decision, you can never trust AI. Use each conflict as a learning opportunity. Analyze why the AI model made a certain decision, refine the model or your approach accordingly, and establish a feedback loop where AI outputs and human expertise are regularly reviewed and adjusted. This continuous learning process improves both the AI system and your ability to integrate it into your decision-making.

O conceito de aprendizado contínuo é uma pedra angular tanto para humanos quanto para sistemas de IA. Cada divergência é uma oportunidade de evolução. A implementação de ciclos de feedback e revisões periódicas de modelos pode enriquecer o desenvolvimento mútuo, levando a um sistema mais adaptativo e preciso. Além disso, incentivaria uma cultura de melhoria constante, onde o aprendizado se torna um elemento chave para a inovação e a competitividade.

Conflicts between AI and human expertise are opportunities for growth. Treat these situations as learning experiences for both the AI model and yourself. Analyze the conflict to understand why the AI made its decision and how your expertise contributed to the final outcome. This continuous learning process improves both AI models and human judgment over time. By embracing these moments, you ensure that the AI evolves and that you remain adaptable, refining your approach to future decisions.

Disagreements with AI models offer valuable learning opportunities. Analyze the outcomes to understand what worked and what didn’t. Use these insights to refine both the AI model and your expertise. Embrace continuous learning to enhance collaboration between human judgment and AI, improving decision-making over time. This iterative process helps both the AI system and your expertise evolve, fostering better outcomes and more effective cooperation in future scenarios.