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Black Box or Glass Box? The Transparency Question for AI Agents
I believe that show transparent the agent should be depends on the needs of the user and the context as well. If the context is pretty simple, like recommending content, drafting a message, then a short rationale should be enough because as mentioned "sometimes users just want the answer quickly". However, in more crucial cases, such as healthcare, finances, business, etc - the agent or system should provide more reasoning and detailed audit trail (if required) to secure trust.
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Keeping Track: Version Control for AI Flows & Prompts
The way that software code is handled, is how I would think to manage AI flows and prompts. Like version control. Changes would also be explained - what was made, why that change was made. Fixed sets would be used to compare the new version to the old version. This way, it would be organized and systematic.
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Design Your Dream AI Agent for the Future
In five years I would like to create an AI agent in the creative space that acts like a creative partner in film and video production. It could give suggestions on light/camera angles, storyboard, animate, add music, create visuals, etc. It will be highly collaborative and would implement the feedback rapidly. I would also want it to ask further questions such as "would you like to add more depth to the scene" so we can brainstorm more. It would help as director and editor, both. I would, however, want to avoid the risk of "losing human touch" or credibility. I would make sure that the agent adds to the vision of the team behind it, without replacing them.
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Can AI Make the “Right” Call in an Ethical Dilemma?
An ethical dilemma faced could be reviewing an employee's interaction during customer service. They might have acted in good faith (for example, offering a refund) but that violates company policy. From the business' standpoint, termination seems just but for overall fairness and company reputation, this matter is excusable. In such cases, AI agents must be trained to provide recommendations on how to deal with these situations instead of an outright consequence. It should also first flag the problem so it can be reviewed by humans and not make final decisions. Agents should also be created and programmed in a way that they ensure ethics like customer loyalty/trust, fairness, etc.
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What If AI Agents Worked as a Team?
Going along with my previous example of healthcare, there could be a scenario of three different AI agents helping doctors with their patients. These agents separately may take of different things (test results history, medical records, possible treatments etc) but if one fails, this hampers the work of the others as well. A proper BRD must be designed to properly outline the objectives and for utmost clarity. Ethical considerations must also be considered with proper resource management. Potential risks must also be assessed beforehand.
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Who is Accountable When AI Goes Wrong?
The AI agent in itself cannot be blamed the same way a human can be as it is not a cerebral or moral agent. In the example of an AI chatbot for a healthcare provider a query goes wrong - the AI agent says yes to a facility not covered by the patient's insurance - things can go awry. The solutions agent responsible for creating the agent and those who overlook the development of it can be held accountable. To ensure transparency I would make sure that proper communication among teams and developers is being done and regular testing is done to ensure accuracy.
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How Can AI Earn Trust in Your Team?
People might feel more comfortable with AI when it starts off by giving suggestions. For example, "Based on your previous interests". This shows the team that AI is available for support so they are more comfortable around it and feel as though they are in control. AI can allow for feedback from users as well. I would provide rating or an option to leave comments based on what was liked or disliked by users. AI language would also be made friendly and easy to understand and comprehend.
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What Should AI Do When Goals Clash?
One of the things that AI must do is weigh its options and decide priorities. For example, in self driving cars, it's goals must be to keep the passengers safe while adhering to traffic rules. If something unforeseen happens, for example something coming in the middle of the road, AI must counter that. To save lives of those inside the vehicle and also outside, it can avoid the rules and focus on the main priority - safety.