You've spent months developing, testing, and refining your AI system, and now it's time to deploy it to your users and stakeholders. But how do you know if your system is actually delivering the value and benefits you expected? How do you measure the impact of your AI system deployment on your users and stakeholders?
Before you launch your AI system, you need to define what success looks like for you and your users. What are the goals and objectives of your system? How will you measure them? What are the key performance indicators (KPIs) and metrics that reflect your system's performance, quality, and outcomes? How will you collect, analyze, and report the data? Having a clear and consistent framework for measuring your system's impact will help you evaluate its effectiveness and identify areas for improvement.
First thing to consuder is the premise. We do not have AI yet. If we did we would definitely know it. We have big data learning algorithms. It's likely binary processing will never provide AI similar to human consciousness. It's is highly unlikely that we are using quantum computing for things like Microspft Bing search either.
One of the most important sources of information about your system's impact is the feedback from your users and stakeholders. How do they perceive and use your system? What are their needs, expectations, and preferences? How satisfied are they with your system's features, functionality, and usability? How do they rate your system's accuracy, reliability, and transparency? How do they trust and understand your system's outputs and recommendations? You can collect feedback from your users and stakeholders through various methods, such as surveys, interviews, focus groups, user testing, reviews, ratings, comments, or social media.
This is standard requirements gathering. How does this pertain to the theoretical AI application. I would think that one would want to focus on clear s ipe limiters and gates to keep an intelligent entity on the tasks required.
Another way to measure your system's impact is to monitor its behavior and performance over time. How does your system respond to different inputs, scenarios, and environments? How does it handle errors, exceptions, and anomalies? How does it adapt to changes in data, user behavior, or context? How does it affect the performance, efficiency, and productivity of your users and stakeholders? How does it align with your ethical, legal, and social standards and values? You can monitor your system's behavior and performance through various tools, such as dashboards, logs, alerts, audits, or testing.
Another way to measure your system's impact is to compare its results with your baseline and benchmarks. How does your system compare with your previous or existing solutions? How does it compare with your competitors or industry standards? How does it compare with your expected or desired outcomes? How does it compare with the best practices and guidelines for your domain and application? Comparing your system's results with your baseline and benchmarks will help you assess its value proposition and competitive advantage.
Finally, you need to evaluate your system's impact on your business and society. How does your system contribute to your business goals and strategy? How does it generate revenue, reduce costs, improve efficiency, or enhance quality? How does it create value for your customers, partners, or stakeholders? How does it support your brand image, reputation, or loyalty? How does it affect the social, environmental, or ethical aspects of your domain and application? How does it address the challenges, opportunities, or risks of AI in your context? Evaluating your system's impact on your business and society will help you demonstrate its relevance and significance.
I believe this will not be relevant. If it makes money or reduces costs enterprise businesses will use ot regardless of ethical outcomes.
How to you limit its scope and keep an AI on a listed narrow objective 100% of the time. How do you turn it off with assurance you can at sny time. How to you stop cloning or sharding.