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9. Imagine a process where the AI agent must balance two or more objectives — for example, minimizing response time vs. maximizing customer satisfaction, or sticking to process rules vs. delighting a VIP client. Describe one such situation and explain how you would guide the AI to handle the trade-off. What logic, rules, or signals would help it make the right call? Situation Case Flight EK 380 has a case scenario where AI should decide to opt one of two passengers to fly. One is the Platinum tier(Highest of all tier) passenger who exactly has arrived one hour prior the flight and Other is passenger who is a blue tier (Lowest of all tier) member who travels in economy class once a year but customer is seriously ill he should travel immediately with doctors recommendation.Unfortunately economy class is completely filled an hour before due to festive season. AI is asked to tactical situation. Conflict - Stick with process vs delighting a vip client. Signals - Key factors Platinum tier passenger To be upgraded immediately to business class most of the time of his travel. Loyalty/ Revennue - Generators $10,000 a year and always fly the same airline Present booking - Economy Blue tier passenger Passenger who serious ill required medical attention Loyalty/ Revennue - Fly the airline at least once a year and $1000 a year Present booking - Economy Operational Parameters - Flight starts 60 min decision to be taken the soonest - Security check and reaching the boarding gate takes 45 mins - Next flight 8 hours later - Compensation to Blue tier $300 dollars and next flight booking - Compensation to Platinum tier $1000 dollars and unhappy customer. AI Rules - Emergencies should be given priority over VIP clients. - Cost of compensation to the airlines. - Loyalty status of the customer AI Decision - Upgrade blue tier passenger with medical emergencies to priortize life risk to upkeep airline PR & advert status which can bring more new businesses to airline. -Platinum tier passenger given a ticket for next flight with free business class upgrade without detecting any points. And offer free lounge access with spa services for the long wait/ lounge access with $200 additional voucher. - AI decision summary points - Platinum tier passenger still satisfied with additional rewards given by the airline and awaits for next flight. Airline makes sure platinum tier passenger is satisfied and loyalty program member is maintained throughout the year. - Blue tier passenger understand the airline compassion and policy towards life risk issues than loyalty or revenue. -both decision are understood by the customer and upheld.

Per my understanding AI would face this kind of trade off in most of the service industries where the AI agent need to handle 2 or more objectives such as time bound, accurate, precise, customer delight, etc. Taking example of Customer Service in E-commerce.. Scenario : Customer queries are been handled by an AI Agent. Here the AI needs to manage response time with accurate and precise replies to queries regarding orders, deliveries, return, refund, etc. How you would guide the AI to handle the trade-off – 1. Identify the Issue/query type : AI should first recognize the type of query if it’s an order status, return, refund or just an information. 2. Assess the urgency : Basis the query and customer responses assess the urgency attached to the query 3. Assess the complexity: such as order status can be simpler to handle whereas lost or refund may need additional information or time to reply. What logic, rules, or signals would help it make the right call? 1. Rules: Set the priority rules such as simpler queries like order status to prioritize for quicker reply over the queries like lost items or refund may need additional information or time to reply 2.Logic: For the prioritized queries response may be shorter with predetermined pattern of text such as delivery time, tracking or details of delivery agent etc. For queries requiring longer time to add follow up question to gain more understanding and give more detailed and precise reply including adding steps needed by customers to take 3. Signals: Customer Response (Important to assess what the customer says during the conversation. Basis those sentiments replies may need changes) and Response Time (Overall response time to be checked to ensure it gives quicker responses per the urgency)

In artificial intelligence domain, outcome of any question (process) depends on the input variables (existing references available datasets) and the algorithms (program) which is developed on available data sets. Incase of any goals clashes, firstly we need to retrospect the input data set for its accuracy, authenticity and purpose. We need to search within the developed algorithm (program) for any errors (e.g. syntax, semantics etc.), or any undesirable clause which can modify or influence the outcome. It may also happen that based on the data, AI agent is exploring new possibilities which has not been heard off and is completely new. Based on thorough human assessment, we can decide the actual outcome. If it still clashes, we can modify the algorithm (through advance discriminator) to achieve the target goal.

In the Human Resources Outsourcing domain (HRO) if for e.g. an AI agent is deployed in the sourcing process to screen candidate profiles and select the best fit, there is a conflicting objective between candidate quality and time to hire. AI agent must identify the best fit for a particular job role and spend minimum amount of time in filtering the profiles. In order to balance these two conflicting objectives, the following methodology can be used – 1. Start by defining clear metrics such as skills, qualifications, experience, geography, time to hire and time spent in each phase of the hiring process. 2. Collect historic data and trends to train the agent. 3. Use Multi Objective Optimization (MOO) techniques such as Pareto Optimality which will find an optimal solution that will improve one objective without worsening another. In order to do so algorithms such as NSGA – II works the best where from the population of solutions each solution is evaluated and ranked based on their Pareto dominance and the best solution is selected. To deploy MOO techniques successfully one must ensure high quality data is collected and used for optimization. Clearly define the objectives and select the best suited MOO algorithm.

When AI goals clashes, it should admit 🙂 . No pretending to know what's “right.” Just flag the message and hand it off to a human, since most of the time, the clash is human fault. If we feed conflicting priorities then AI will clash. So modify instruction after getting notified for clash.

In today’s world of widespread use of AI, it often happens that AI is presented with a Dilemma of Goals or Objectives clashing with each other. Making Humans as well as AI to choose one or the other. The Very reason why people approach AI is to get solutions or seek advice and suggestions which they themselves are unsure about. I think for this Decision-making AI should prioritize ideas or suggestion on some pre-defined criteria of Safety, Principles, legality and established values of right and wrong Check if the goal might cause harm, is it safe, is it ethical. AI should seek clarification by asking probing questions like a) what is the priority, is it one of the constraints like cost or time, is it speed or accuracy. What is the user’s preference b) If a suggestion solution in troubleshooting a tech issue is going to take too long to implement and impact volume handling, AI needs to check what is the criticality, Severity and priority for the business while selecting the middle ground between optimising efficiency and accuracy in a Tech troubleshooting scenario c) E.g. if it is a choice of driving route, do you need to reach there faster or do you want a shorter route to consume less fuel d) E.g. if it is medical technology goal, whether the advice given safe, is considering all possibilities, tried and tested or high-risk, and high potential of success of a surgery or treatment plan AI should also apply abstract, contextual and logical reason before deciding on a goal. what could be possible short team and long-term gains vs consequence, is it safe? Check if there is enough data fed in AI to have best conflict resolutions from historical data. For e.g. if a transportation cab service company like Ola or Uber. If the goal is to reduce cost by introducing self-driven vehicles, it needs to consider if the demographic facts of a region and not just roads, routes and feasibility. So, when we ask AI for goal choice it should give region specific answers like in a western country the population is less. Roads and traffic are structured and self-driven car is possible. However, AI after studying dynamics in a region like Asia, should recommend against the goal given the increased number of variables in safety and feasibility. All in all, if AI is conflicted between goals, it should seek human judgement.