Leaderboard

I have some thoughts on how orienting, training, communicating, and involving team members would lead AI to earn trust: * To inculcate transparency, workshops need to be held where we demonstrate how AI works on customer (internal and external) queries and generates responses from available knowledge bases and from the web. * When deploying AI in a particular department, the inputs and feedback from team members need to be gathered . Create a channel or forum for them to share issues, improvements, and suggestions. This channel/forum should be looked at by Project Managers to review and act on the inputs from team members * Training sessions and plans to educate the team should be created and implemented. Once the team understands the working of AI and the value they bring, they will be more confident and shall trust the technology * Another way to earn and build trust for AI among the teams is by ensuring AI applications are built on the foundation of privacy and data security and that they comply with such policies and relevant regulations. * Communicating and showcasing tangible benefits produced by AI in day-to-day tasks. Making everyday tasks easier or more efficient would lead to earning trust in AI. e.g., improved efficiency, reduction in the time taken to resolve customer queries, increasing accuracy in data entry etc., will

What is trust and why is it crucial in organizational setting: Trust can be defined as a strong belief in the character, capability, or truth of a person or something. In other words, trust is a byproduct of reliable consistent and transparent behavior of someone or something overtime. Trust is one of the essential characteristics of a person within any setting, that determines dependability which influences success. Likewise, gaining trust with the introduction of AI agents within organizations is paramount to its success. Scenario: Let us imagine the Facilities Management (FM) company introduces an AI agent to help its management by analyzing technicians’ performance data and developing performance evaluation reports. The objective is to minimize manual administrative efforts and produce fast and reliable reports. This is a very sensitive area and hence producing a more consistent, accurate, transparent and unbiased report is paramount to gaining trust from the management and technicians that shall determine success of the rollout. Strategies for creating AI agents that inspire trust: Clear understanding of the use case (business requirement) and selecting the appropriate AI application / platform incorporating the following capabilities is a core necessity. 1. Incremental progress Start small by identifying and implementing small portions of evaluation and progress steadily by incrementing scope. This helps to focus and edify the agent to increase accuracy and build trust overtime. 2. Intent clarity During the initial setup, clearly defining the intent of the application using prompt engineering concepts will eliminate ambiguity. This will lead to the agent providing relevant and contextual insights eliminating errors during the initial stages and moving forward. 3. Data reliability Ensure to provide and use quality data to train the AI system and develop. Inclusivity of all possible scenarios will help eliminate bias in the system output. 4. Explainability Develop system capability to be able to clearly explain the inputs, logical steps and the rationale behind the insights. This will help eliminate the black box effect in the AI system. 5. Traceability Establish AI system performance metrics record logs and action steps to understand where and how the system went wrong. Continuous performance monitoring shall help us understand how the system is performing and maturing over time. 6. Accountability Decide and communicate through policies, key responsibilities and accountabilities for the inputs, rules, insights and the ones responsible for the overall system performance. 7. Feedback looping Build capabilities in the system to be able to accept feedback from users, experts and developers for the systems ongoing learning and fine tuning. Effective feedback loops place a significant role in the progressive performance of the AI system. 8. Security All data used to train, develop and deploy the system must be secure and private to avoid the spread of sensitive information. Following and adhering to these strategies shall be beneficial to building trust over the system and for successful deployment and application.