Nisha R

I can think of an example where in A company uses an AI tool to help with ISO 9001 audits, which check if businesses meet certain quality standards. The AI looks at data, finds problems, and writes reports. During an audit, the AI mistakenly says a process that is actually fine is not meeting the standards. This mistake causes the company to get a bad report, hurting their certification and reputation. In this scenario, accountability can be distributed to 1) AI Agent: The AI made a mistake, but can't blame the AI alone. The AI's actions depend on how it was programmed and the data it was trained on 2) Developer: The designers and developers of the AI system are mainly responsible. They need to make sure the AI is properly programmed and tested to handle different situations. In this case, the AI's mistake shows there was a problem with how it was designed and tested 3) Human Reviewer: If a human was supposed to check the AI's actions, they would also be partly responsible. The human should have systems to spot and fix mistakes before they become bigger problems. 4) Management: The management team that decided to deploy the AI without sufficient safeguards or oversight also bears responsibility. They should ensure that proper protocols and fallback mechanisms are in place. For transparency and traceability: 1) Make sure a human checks and approves important decisions made by the AI, like flagging problems. This helps prevent mistakes from happening without someone noticing. 2) Regularly check the AI system to make sure it's working correctly. This means looking at its decision records and updating its training with new data to make it more accurate. 3) Keep detailed records of everything the AI does. This helps track down where and why a mistake happened 4) Give clear reports to stakeholders that explain how the AI makes decisions, how often it makes mistakes, and what is done to fix those mistakes. This helps build trust and accountability. 5) Create systems that automatically stop or highlight potentially harmful actions for review before they happen. 6) Set up a system where companies can report problems caused by AI decisions. Use this feedback to keep making the AI better.

We conduct an annual scoping of clients based on revenue priority and headcount to determine which clients will be included in our audits. Throughout the year, some clients may fall out of scope due to a decrease in revenue or a reduction in headcount. These clients, now considered out-of-scope, are treated as exceptions and require manual handling after obtaining approval from the relevant stakeholders. To streamline this process, we propose utilizing AI to identify these exceptions. The AI will analyze input files that are regularly updated with client information. By doing so, it can promptly detect clients that have moved out of scope and need to be addressed. This approach eliminates the need for manual checks each time, ensuring a more efficient and accurate identification of exceptions. The AI system will provide alerts or prompts for these exceptions, allowing stakeholders to review and approve them without the repetitive manual effort. This automation not only saves time but also reduces the risk of oversight, ensuring that all exceptions are handled appropriately and in a timely manner.

In many of our accounts AI is integrated with payment systems like processing payments, refunds, creating invoices, vendor creation etc. To take an example for refund, if AI detects any exception (like approval required for higher refund amount, any ambiguity or exceptional scenario) then trigger goes to the supervisor for approvals. Since the AI handles rule based workload, it also ensures fast service while maintaining consistency and customer satisfaction.

Giving feedbacks and one on one with the staff is "too human" to handover to AI. AI can be valuable contributor by providing data driven insights, identifying opportunities and can free up manager's workload to focus on more important things.