Understanding Automated Decision Making

In daily life, we often make decisions based on incomplete information and uncertainty. This is because we rarely have access to all the facts or a clear view of future outcomes. Humans navigate this uncertainty using a combination of experience, intuition, educated guesses, and by weighing the pros and cons of different options. We essentially build and evaluate arguments based on the information we have, even if it's incomplete. Translating this human approach to decision-making into a computational context is where computational argumentation plays a crucial role. It involves programming AI systems to mimic the way humans reason with incomplete information. This is done by: 1. Modeling Arguments and Counterarguments: AI systems are programmed to represent different perspectives or solutions as arguments, similar to how a human would consider different sides of an issue. 2. Dealing with Uncertainty: These systems are designed to handle uncertainty in data or knowledge. This might involve assigning probabilities to different arguments or considering the reliability of information sources. 3. Evaluating Arguments: The AI evaluates which arguments are stronger, taking into account various factors like the amount and quality of evidence supporting each argument. 4. Dynamic Learning: As new information becomes available, the system updates its arguments and evaluations, much like a human revising their opinion in light of new facts. 5. Prioritizing and Decision-Making: Finally, the system uses these evaluations to prioritize certain arguments and make a decision, even if it's not based on complete certainty. Computational argumentation is crucial for AI systems in areas like law, medicine, and public policy, sales, where decisions often have to be made with incomplete information. By programming AI to reason in a way that's similar to human argumentation, we enable these systems to make more informed, nuanced, and context-aware decisions. Introduction to computational argumentation: https://lnkd.in/efRtgr6w Deeper insights and methods in this Paper: https://lnkd.in/ez5RRQeB

Ever wondered how AI agents actually make decisions? Here's your 60-second guide to the magic behind autonomous AI. No jargon. Just clarity. Think of AI agents like a Formula 1 pit crew. Lightning fast. Perfectly coordinated. Always learning. The 4-Step Race of AI Processing: 1️⃣ Data Pit Stop → Captures your request → Scans its knowledge vault → Checks available tools → Reviews past experiences 2️⃣ Strategy Room → Analyzes the situation → Spots critical patterns → Sets clear objectives → Maps the execution route 3️⃣ Command Center → Weighs all options → Selects optimal tools → Simulates outcomes → Adapts strategies live 4️⃣ Victory Lap → Executes with precision → Tracks performance → Banks new insights → Optimizes for next run The mind-blowing part? This entire race happens faster than you can blink. Why it's revolutionary: Traditional AI follows a track. These agents redesign the circuit. Think about it: They don't just process. They strategize, adapt, and evolve. Just like the best racing teams. 💡 Bottom Line: Tomorrow's AI isn't about following instructions. It's about mastering the art of adaptation. ♻️ Share to accelerate AI understanding 👉 Follow Gabriel Millien for more tech insights, simplified Thanks to Prem N. for this great visual. Give him a follow!

I see many stories of people using AI systems to do things it doesn't seem like they were designed to do. That leads to frustration and potentially unsafe applications. But there's not been a formal way to understand the role of AI based tools. They get made, demonstrated, then put into the hands and imaginations of the users. I've been thinking about the SAE framework for autonomous vehicles that describes the role of the driver in different levels of assistance tools starting from warnings (lane departure) all the way to full autonomous driving (https://lnkd.in/esVjzxav). I think we need to apply that kind of thinking to other industries with AI assistance tools. Is this intended to make decisions for me, or just inform me of choices? The design of the AI tools don't really differentiate right now. This paper lays out a model for how to think about the role of the user as AI tools progress in agency and autonomy, and I love how they disambiguate those terms. https://lnkd.in/erWKVatk You can take that framework and create examples for your own company both for internal capabilities and the AI enhanced products you are building. This would serve as a roadmap, help you understand where you are investing, set clearer expectations with users, and ensure product design matches intention.