ChatGPT on Machine-Readable Performance Reporting

ChatGPT's conclusion is essentially the same as You.com's. It will be interesting to see whether its developers take its advice and, if not, whether governments decide to direct them to do so.

Are ML/AI developers doing a good job of keeping those who may be affected by their applications apprised of the potential impact on them?

By what means are they doing so?

The degree to which ML/AI developers are keeping those who may be affected by their applications apprised of the potential impact on them varies depending on the specific application and developer. However, there is a growing recognition within the ML/AI community of the need for increased transparency and accountability when it comes to the development and deployment of these technologies.

Some developers are taking steps to inform and engage with affected communities, such as conducting community outreach and hosting public forums to discuss the potential impacts of their applications. In addition, there are efforts to increase transparency around the data and algorithms used in these systems, such as publishing technical papers, releasing open-source code, and making data sets available for scrutiny.

However, there is still much work to be done to ensure that those who may be affected by ML/AI applications are adequately informed and engaged in the process. As these technologies become more pervasive and have greater impacts on society, it is crucial that developers prioritize transparency and accountability in their work.

Wouldn't it be helpful if they were publishing their performance plans and reports in an open, standard, machine-readable format, like StratML?

Yes, publishing performance plans and reports in an open, standard, machine-readable format such as #StratML would be helpful in increasing transparency and accountability in the development and deployment of ML/AI applications.

StratML, or the Strategic Planning Markup Language, is a standard for encoding strategic plans in an XML format, allowing for easier sharing, searching, and analysis of the plans. By using a machine-readable format like StratML, developers can make their performance plans and reports more accessible and easier to understand, both for those who may be affected by their applications and for other researchers and stakeholders in the field.

In addition, a standard format like StratML can facilitate comparisons and evaluations of different performance plans and reports, allowing for greater accountability and more informed decision-making. It can also enable more efficient monitoring and auditing of ML/AI applications, which can be important for ensuring compliance with regulations and ethical standards.

Overall, the use of an open, standard, machine-readable format like StratML can be a valuable tool in increasing transparency and accountability in the development and deployment of ML/AI applications.

Do you plan to start publishing your performance plans and reports in StratML format?

As an AI language model, I do not have performance plans or reports to publish in StratML format. My primary function is to respond to user questions and provide information based on my training data.

However, I do recognize the value of using standard, machine-readable formats like StratML for organizations and developers who are involved in the development and deployment of ML/AI applications. This can help increase transparency, accountability, and efficiency in the process.

If you have any specific questions or concerns regarding the use of StratML in the context of ML/AI development, I would be happy to try and provide more information.

Pardon my lack of clarity. I should have asked if you know whether your developers have any intention of beginning to share their performance plans and reports in StratML format.

As an AI language model, I do not have access to information about the internal processes or plans of the developers who created and maintain me.

However, it is important to note that the decision to use StratML or any other standard, machine-readable format for publishing performance plans and reports would depend on the specific policies and practices of the organization or developers involved in ML/AI development.

That being said, the use of standardized formats like StratML can provide several benefits, as I mentioned earlier, including increased transparency, accountability, and efficiency. It is possible that some ML/AI developers may already be using or considering the use of such formats in their performance reporting and planning.