Agentic AI is here and ethics can’t be an afterthought
We’re entering the third wave of AI, known as agentic AI, and it’s reshaping how we work and live. Imagine an AI system that, in addition to analyzing your medical scans, autonomously coordinates with specialists, schedules follow-ups, and adjusts treatment plans in real-time. This isn’t science fiction anymore. It’s happening around us across industries, from finance and retail to healthcare and cybersecurity. Read my previous blog about what makes agentic AI the future of autonomous intelligence.
Gartner predicts that by 2028, 15% of daily work decisions will be autonomous, up from 0% in 2024. That’s a massive shift in how critical choices affecting our lives get made. That’s why developers, businesses, and governments need to act now. Regulations like the EU AI Act are leading the charge, but we should not rely solely on policies. This blog delves into the ethical complexities of agentic AI and the crucial steps we must take to ensure this powerful technology serves everyone fairly, not just efficiently.
Why agentic AI systems need ethical frameworks
Traditional AI systems followed strict rules and required constant human oversight. Agentic AI is different because it operates like an independent team member. It can analyze data, select the appropriate tools, and take action without waiting for approval.
Consider a hospital AI that monitors inventory levels and automatically orders supplies when its stock runs low. Or an IT security system that can detect a cyberattack and immediately block suspicious activity. These capabilities can save time and avoid problems, but they also create new risks.
The autonomous nature of these systems means that mistakes get amplified quickly. If an AI system has biased training data, it might unfairly deny loans to thousands of applicants before anyone notices the pattern. If it misinterprets a task, it could send confidential information to the wrong recipients or delete essential files.
Then there’s the transparency challenge. Many agentic AI systems are so complex that even their creators struggle to explain precisely how they make decisions. If an AI flags you as a credit risk, how can you appeal the decision if no one understands the reasoning behind it? This “black box” problem creates serious accountability gaps.
The data foundation problem with agentic AI
Modern AI systems consume enormous amounts of data. Your smartwatch tracks your activity, your credit card records your purchases, and your smartphone monitors your location. Estimates suggest that we generate around 400 million terabytes of data every day.
Agentic AI systems utilize this data to make predictions about behavior, assess risks, and inform decisions that impact a person’s life. These AI systems not only store this information but also analyze it. They also actively use it to make predictions and decisions about your life. The challenge is that most people are unaware of how much data they share or how it’s being used. Those lengthy, complex agreements are often deliberately designed to be confusing. Research shows that very few users read these documents thoroughly. As a result, people remain unaware of how their data might influence an AI’s decision about their job application, insurance rates, or loan approval.
This lack of informed consent creates a trust problem. When people don’t understand how their data is being used, they lose confidence in the systems that rely on that data. This is especially concerning when AI systems utilize personal information in unexpected ways, such as analyzing…..Read More
