Q 759. 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?

 

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In our process as auditors AI must balance between sticking to policies set by the company VS working around business needs. Let's say we do have a policy that would require us to conduct calibration every quarter however the clients wants it to be done on a monthly basis. First you I will set the criticality of the policy for the AI to weigh in the risk of bypassing the said step then will set the bias of the decision for each policy if more for client or the policy. 

When goals clash AI should be able to understand which goal gives maximum benefit along with minimizing any issues or disrupt the overall project.  AI should be able to follow some logics like, what is the core priority, expected benefits, what are the constraints, check regulatory compliance requirements, inputs from stakeholders

Example Scenario:

 

A VIP client urgently requests an audit status update. The AI agent faces a dilemma:

• Quickly provide a preliminary report to meet the client’s immediate expectations.

• Take additional time to ensure thorough verification and accuracy, thus guaranteeing high-quality, error-free information.

 

Guidance on Handling the Trade-Off:

1. Set Clear Priority Logic:

• VIP client requests could have a higher weight in customer satisfaction, allowing the AI to prioritize quality and personalization slightly over speed.

2. Threshold-Based Rules:

• Define response time thresholds based on client tiers. If a VIP client requires immediate attention, the AI can provide a high-level interim update immediately, clearly noting it is preliminary, followed quickly by a detailed and verified follow-up.

3. Risk and Materiality-Based Signals:

• The AI should assess materiality and risk associated with information accuracy. High-risk or material audit areas should prioritize accuracy, delaying slightly if needed to ensure reliability.

4. Transparency in Communication:

• The AI should communicate clearly about the preliminary nature of quick responses and manage expectations by providing clear timelines for more detailed updates.

5. Feedback Integration:

• Capture client satisfaction data after interactions to continually improve how the AI balances these trade-offs. AI models should learn from historical client feedback to refine decision-making algorithms continually.

 

Decision Logic Summary:

• If VIP & urgent: Provide immediate interim status → clearly communicate preliminary nature → prioritize comprehensive follow-up.

• If non-urgent or standard client: Maintain balanced thresholds with clear emphasis on accuracy and thoroughness, optimizing response time accordingly.

 

Implementing these logic rules and signals ensures the AI makes informed, balanced decisions, enhancing both responsiveness and client satisfaction in audit processes.

When an AI agent faces two competing objectives, it needs a framework to guide its decisions. This framework can rely on factors like the importance of each objective, the context of the situation, customer feedback, and predefined rules or priorities. The AI can also adapt over time based on learning from past outcomes to make better trade-offs.

For example, An AI hotel assistant must balance two goals: following company policies (like a strict cancellation fee) and delighting a loyal customer. Imagine a frequent guest requests a last-minute room cancellation without the usual penalty due to a personal emergency. The AI needs to decide whether to enforce the fee or make an exception.

How the AI handles it:

1. Identify the Guest: The AI recognises the guest as a frequent and high-value customer based on loyalty points or booking history.

2. Apply Flexible Rules: While the default is to apply the cancellation fee, the AI is designed to allow exceptions for loyal guests in special circumstances.

3. Factors to Guide the Decision:

   • Sentiment Analysis: If the guest sounds distressed, the AI prioritizes empathy and offers a waiver.

   • Long-term Value: It considers how waiving the fee could strengthen loyalty and lead to more future bookings.

   • Context: The reason for cancellation (e.g., an emergency) might justify relaxing the policy.

Imagine this situation:
An AI assistant in a hotel is helping guests. One VIP guest asks for a special meal at midnight, but the hotel rules say the kitchen closes at 11 PM.

Here, the AI must balance two objectives:

1. Following rules (kitchen closes at 11 PM)

2. Delighting the VIP guest (giving great service)

How to guide the AI to handle the trade-off:

I would give the AI a clear set of priorities and signals to follow:

1. Identify who the guest is – If it's a VIP, that’s an important signal.

2. Check the rule – Kitchen closes at 11 PM. That’s the default.

3. Look for flexible options – Can the AI ask the chef if something simple can be prepared? Or offer a special cold meal or snack that’s allowed after hours?

4. Log the decision – If it bends the rule, the AI should note why (VIP guest, special case).

The logic for the AI would be:

If breaking the rule has a high positive impact (like delighting a VIP) and low risk (like offering a pre-prepared meal), it’s okay to softly bend the rule.

But if the risk is high (e.g. health/safety issues), it must politely say no and offer the best alternative.

This way, the AI acts smartly and keeps both service and safety in balance.

Conflict Resolution in AI Agent Goal Prioritisation

Scenario:

Assume there is an AI agent is deployed to support enterprise users by streamlining day-to-day tasks, ensuring rapid responses, and delivering accurate information. The agent operates under predefined service prompts to enhance user efficiency while maintaining information precision.

Goals : Accurate and faster response. 

Task:

A conflict arises when a user query requires extensive data retrieval for accuracy, which subsequently leads to longer processing times, impacting response efficiency. The agent must balance speed and data accuracy without degrading the user experience.

Conflict arise: Accuracy impacts the response time. 

Action:

Solution options: Consideration to manage the objective conflict  

To resolve conflicting objectives, the AI agent must follow a well-defined prioritisation framework. Key considerations include:

1. Hierarchy of Agent Prompt instructions or Business Rules – Explicit prioritisation must be established within system logic to determine whether speed or accuracy takes precedence in various scenarios. Agent should be able to extrapolate based on the instruction for specific scenario. 

2. Real-Time User intervention  – If ambiguity exists or decisions fall into a gray area, the AI agent should prompt user interaction to refine requirements and determine the next course of action.

3. Incremental update and Process Transparency – The AI should provide intermediary results or partial responses when full processing is expected to take longer than acceptable limits. Continuous status updates should be communicated to users, ensuring they are informed of system operations and expected wait times.

4. Adaptive Prioritisation – The AI agent should incorporate runtime decision-making logic, dynamically adjusting priorities based on context, urgency, and user-defined preferences.

Result:

By implementing structured prioritisation strategies and maintaining transparency, users remain informed about AI processing status, allowing them to intervene if needed. The agent adapts its behaviour based on situational constraints, ensuring that response latency does not compromise overall service quality. Prioritisation logic can be predefined or dynamically determined at runtime to optimise efficiency while preserving accuracy.

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.

  

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. 

Let's say the AI agent supports the customer service team for an airline company and helps customers with reservations, flight schedules, refunds etc.

 

Situation: A VIP customer contacts the AI to modify a flight reservation due to a family emergency. The AI agent must rely on standard scripts to ensure minimum response time for other customers waiting in queue, without risking appearing impersonal or lacking empathy.

 

Task: The Agent must

1. minimize wait time and response time,
2. follow process/organizational policies, and
3. ensure customer satisfaction beyond expectancy (delighting the customer).

Action: As an AI solutions architect, I would guide the AI with the below:

• Design the agent with an ability to process multiple queries simultaneously through Session Management and scalability.
• Design a response strategy for the agent based on priority hierarchy using ability to analyze urgency, sentiment and customer status. If the situation is flagged as critical, customer satisfaction would take priority over rapid resolution. To do this, the agent would switch from standard verbiages to an empathetic approach. It would also prioritize actions that address the root concern.
• Design predictivity and offer solutions based on historical data.
• The agent would be aware of overall queue length and adjust responses dynamically. If wait time spikes, it would be able to optimize by blending personalization with rapid resolution techniques.
• The AI would be able to evaluate feedback loops to refine decision making.
• Logic - The AI would be designed to work like below in real time:
  1. Signals:
     • Customer sentiment and urgency level.
     • Queue length and current wait time
  2. Rules:
     • Assign higher priority to satisfaction for critical cases.
     • Assign higher priority to response time for non-critical cases.

Result: Below is the dialogue that followed after the customer explained his situation to the agent.

• Agent: I'm so sorry to hear that, Sourav. Let me help you right away. I see you have an active reservation, ID ABCD1234. Please confirm if you want to modify this reservation itself. <Provides option, a) Yes, that is it. b) No, I have another ID.
• Sourav: Yes.
• Agent: Thank you for confirming the details, Sourav. Please confirm your preference. <Provides option, a) Cancel the reservation, b) Prepone/Postpone it>
• Sourav: Cancel the reservation.
• Agent: I will get to it right away. Just wanted to let you know that our regular cancellation fee is usually 50%. However, as you have a valid VIP subscription with us, the cancellation fee is reduced to 40%. Please confirm if you agree with the cancellation fee and want me to proceed further with the cancellation. <Provides option, a) I agree, please cancel my reservation b) Hold on, tell me more about the cancellation fee.
• Sourav: I agree, please cancel my reservation
• Agent: Thank you for confirming, Sourav. I have successfully cancelled your reservation; your cancellation confirmation ID is CANABCD1234. The amount will be credited to your bank account within next 2 business days. You can download the cancellation receipt from the website by logging into your account, or I can share it with you now. <Provides options, a) I want the cancellation receipt now, b) That's okay, I will visit my account later, thank you.>
• Sourav: I will visit my account later, thank you.
• Agent: You are very welcome. Keeping your present situation in mind, I have added a one-time 10% discount coupon in your account which you can use to book your next flight. You can access this coupon by logging into your account -> My coupons. Is there anything else I can assist you today, Sourav?
• Sourav: Thank you so much. You’ve been a big help.
• Agent: You’re very welcome! I hope everything goes smoothly for you and your family. If you need further assistance, don’t hesitate to reach out. Safe travels.

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.

Prioritize goals- Use hierarchy or weighting system or use mutli objective optimization

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.

Many organizations face business exigencies where multiple critical goals must be achieved within overlapping or conflicting timelines. Current or routine way struggles with Resource allocation, Timeline, and Priorities between departments.

An AI-driven approach can help to optimize the above issues efficiently.

 

1. Scheduling & Prioritization

A company has its data on resource availability, team capacity, and historical performance. After analyzing these data, predictive analysis to be done along with scheduling algorithms to adjust timelines.

 

2. Resource/workforce optimization

Based on AI-driven evaluation of employee skills and workload, tasks are assigned. This approach helps to predict and mitigate potential roadblocks.

 

3. Predictive Risk identification & Solution strategy

A specific AI-driven forecast helps to identify potential delays and recommends advanced actions. The example also includes a Monte Carlo simulation in which alternative timelines can also be evaluated.

 

4. AI-driven team negotiation

With the help of AI, a fruitful negotiation to be done between teams or between departments. This considers the current workload, business requirements, and stretch capabilities.

 

5. Real-Time Project Tracking & Proactive Planning

Similar to a project management team, AI continuously reviews milestone achievements and adjusts the timeline according to business needs. AI also considers the actual time required for each task as per historical knowledge.

Conclusion

AI-driven solutions help to refine schedules, optimize resource/workforce, and mediate between teams or departments to complete goals as per stipulated timeframes. In this way, a positive and synergistic output can be achieved without delaying timelines so that all goals can be completed in the defined time frame.

Taking an example of the travel industry where AI agent needs to minimize response time and maximize customer satisfaction. Additionally, the agent would need to adhere to strict process rules while also delighting VIP clients.

Scenario:

A VIP (High value) client contacts the AI agent with an urgent request to change their travel itinerary due to an unforeseen event. The client would expect a swift response because their travel plans are time-sensitive. The change involves multiple flights and hotel bookings, requiring careful coordination to ensure a seamless experience.

AI agent should be guided on below points - 

1) Recognize the VIP status of the client and prioritize their query. Use client tags, account history, and previous interactions as reference points

2) An initial acknowledgment should be sent within a very short time frame (e.g. within 5 mins) to reassure the client that their issue is being addressed. Use personalized messages by addressing client by name and specifying their issue in the response. Eg - Hello Joe, thank you for reaching out to us. We understand the urgency of your request to change your travel itinerary. We are currently reviewing the details of your travel plans and will provide a comprehensive update shortly. Your satisfaction is our top priority.

3) Transparent and regular communication with expected TAT & steps taken would be key to satisfying the client, Eg - Hi Joe , we wanted to keep you informed about the progress of your itinerary change. We are currently finalizing the rebooking process and expect to have everything confirmed by XX hrs. Thank you for your understanding.

4) Process rules and protocols to be adhered for maintaining compliance to company policies

5) Document and record steps taken for traceability and for future reference

6) Check if human intervention is preferred, look for emotions and respond accordingly

7) Monitor client responses and feedback to gauge satisfaction and adjust the approach accordingly

😎Final response to summarize steps taken to resolve client query, with reference number and a link or number to reach out to for any further issues

 

•    Contextualize the situation and opt for the best fit: AI can check the context and pick the best option that applies to the scenario.
•    Explore alternatives by tracing historical data to pick the most suitable option: By running an analysis on past records, AI can pick the best alternative outcome. 
•    Leverage stakeholder priority to determine what is the best option: AI can prioritize stakeholder interest and use that to give the best suitable outcome that aligns with human values and interests. 
 

In Sales Operations process - Quote Cycle time is an important parameter to ensure timely and accurate quotes for clients thereby ensuring good customer satisfaction.

In this scenario, AI can help in ensuring correct inputs and mapping (e.g. Product #, Client ID, Quantity etc) are logged into the quotation basis client requirement.  By ensuring the right level of inputs, it checks the quality of the inputs and timely submission of quotes with minimum turnaround time.

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. 

 

 

 

 

 

AI should begin by identifying goal clashes. It should prioritize the most crucial goal considering different aspects, e.g. safety, and ethical rules. If possible, AI should try to balance between the goals. In critical sectors like healthcare, AI should ask a doctor before making a decision or follow strict pre-defined ethical rules.  

For example, a self-driving car is given two goals – follow the traffic rules and reach the destination quickly. The car sees a red light and is running late, thus, there is a clash between the goals. Here, it must prioritize safety over speed to meet its goal.

As an audit team, meeting defined timelines is a key responsibility. However, timely completion should never come at the expense of audit quality. Each audit we conduct must be thorough, with careful and complete validation of the evidence provided. The credibility of our findings depends on the diligence we apply in reviewing documents, assessing controls, and identifying gaps. As auditors, we carry the responsibility of upholding the highest standards of accuracy, objectivity, and integrity. In an environment where speed is often prioritized, it's crucial that we strike the right balance between efficiency and thoroughness. In this context, I am particularly interested in exploring how artificial intelligence can assist us in maintaining that balance—streamlining routine tasks, enhancing evidence validation, and helping us focus our efforts where they are most needed. By leveraging AI, we can potentially ensure that audit quality remains consistently high, without compromising on delivery timelines.

When AI systems encounter conflicting goals, they need to employ strategies to balance and prioritize these objectives effectively. Here are some approaches that AI can take when goals clash:

1. Goal Prioritization: AI systems can prioritize goals based on predefined rules or the importance assigned to each goal. For example, in a customer service scenario, minimizing response time might be prioritized over maximizing customer satisfaction if quick responses are critical.

2. Negotiation Mechanisms: AI agents can use negotiation mechanisms to find a compromise between conflicting goals. This involves evaluating the trade-offs and finding a middle ground that satisfies multiple objectives to an acceptable degree.

3. Utility-Based Decision-Making: AI can use utility-based decision-making to evaluate the potential outcomes of different actions. By assigning utility values to each possible outcome, the AI can choose the action that maximizes overall utility, balancing the conflicting goals.

4. Contextual Analysis: AI systems can analyze the context in which the goals are being pursued. This involves understanding the specific situation and dynamically adjusting the priorities of the goals based on real-time data and contextual information.

5. Learning from Feedback: AI can learn from past experiences and feedback to improve its decision-making process. By analyzing the outcomes of previous decisions, the AI can adjust its strategies to better handle conflicting goals in the future.

6. Ethical Considerations: AI should also consider ethical implications when balancing conflicting goals. Ensuring that the chosen actions align with ethical standards and do not cause harm is crucial in decision-making.

By employing these strategies, AI systems can navigate situations where goals clash and make informed decisions that balance multiple objectives effectively

A customer service AI must balance minimizing response time and maximizing customer satisfaction. A VIP client contacts support with a complex issue.

Guiding the AI:

1. Contextual Analysis:

   • Customer History: Identify VIP status and past interactions.
   • Issue Complexity: Assess the complexity of the issue.

2. Prioritization Logic:

   • Urgency and Impact: Quick initial acknowledgment for urgent issues.
   • Customer Satisfaction Metrics: Prioritize detailed responses for dissatisfied customers.

3. Dynamic Response Strategy:

   • Initial Acknowledgment: Send a quick message to minimize response time.
   • Detailed Follow-up: Provide a thorough solution after initial acknowledgment.

4. Feedback Loop:

   • Customer Feedback: Gather feedback to adjust strategies.
   • Learning from Interactions: Improve decision-making based on past experiences.

Taking into consideration a hypothetical situation similar to what was discussed during class. Let us take into consideration a Loan approval process where AI processes applications and follows a rule-based approach, all applications above a specified threshold based on say payback ability / solvency or collateral fulfillment of over 40% would be approved any applications which fell below that threshold would be rejected. In this case, it is tricky for the AI to balance high customer satisfaction (typical human psychology is when something gets rejected, they are not satisfied) and following process and protocol and thus must strike the balance between contradictory priorities. Possible solutions for the AI to handle this while increasing or improving CSAT would be:

 

1. AI is allocated high compute bandwidth and also opens additional dialogue with customers who fall short withing 5% of the threshold (35-40%) for instance, where customers can present additional documents to support their case and add other collateral assets which could result in a quick approval despite not being within threshold.

2. If customers are too far from the threshold, the AI could respond quickly informing them of the discrepancy and give them options of how they could meet the threshold or improve their score to be approved for the loan, this would ease the burden of rejection from the customer/client resulting in improvement of the CSAT while striking a balance with protocol/policy.

 

Overall, as a result of either or both of the options training the AI would these sort of options would then help balance Process vs Customer satisfaction as even in the case of rejection the customer gets potential guidance on what can be done to obtain a future approval thereby taking the role of a humanoid customer relationship agent who is trying to help them rather than present a flat out rejection when criteria is unmet.

A great example of balancing multiple objectives is in Customer support chatbots, where the system needs to minimize response time (for efficiency) while maximizing customer satisfaction (for quality). These two goals often conflict—fast responses might lead to incomplete answers, while highly personalized responses might take longer.

 

To Handle this situation, we can prioritize the query types into Simple queries to prioritize Speed and Complex or emotional queries to prioritize satisfaction. We may introduce some Smart rules like - 

1. If customer sentiment is negative or confused, favor satisfaction.

2. If queue length is high and query is low-impact, favor speed.

3. Never exceed a max response time of XX seconds, even when optimizing for satisfaction.

 

Scenario

Let's take an example of a Quality Process of a VIP client where Quality checkers have to follow the Quality Checklist to remain within the scope and timelines and escalate, any possible non-compliance which may have a higher dollar impact, to the Quality Manager for further investigation.

 

Trade-off

Following the checklist will ensure the Quality process remains within the scope and timelines; however, if any transaction gets escalated for further investigation, it may delay the process timelines but may help in detecting a possible major non-compliance

 

AI Implementation

AI should follow a risk-based approach (as per ISO 9001) to assign risk score, based on some rules, like,

• Possible defect impact exceeds a pre-defined financial threshold of $5000
• Analyze the past NC data for high-risk patterns. Higher score for high-risk patterns.
• Analyze already deployed controls and check for their effectiveness. Low scores for weak controls.

The Logic

AI will follow the logic below:

IF the transaction's possible defect impact >$5000 AND past NC risk pattern score is >5/10 OR the deployed control effectiveness score is <5/10, the transaction will be escalated to the QA Manager for further investigation.

 

This way AI will logically decide which transactions require further investigation.

The Goal clashing scenario will come into picture when AI is not able to decide which rule  to prioritize. For eg. in an automatic car driving case AI can get confused with safety and efficiency in certain situations . In such cases human and AI collaboration in decision making  or monitoring the performance of the AI in various situations and evaluating its behavior to handle goal clashes can prove useful . 

While designing the AI system goals and priorities should be clearly defined so that there are no clashes and the desired outcome can be delivered easily.