This is the second part of a 2-part series on the growing importance of teaching Data and AI literacy to our students.  This will be included in a module I am teaching at Menlo College but wanted to share the blog to help validate the content before presenting to my students.

In part 1 of this 2-part series “The Growing Importance of Data and AI Literacy”, I talked about data literacy, third-party data aggregators, data privacy, and how organizations monetize your personal data.  I started the blog with a discussion of Apple’s plans to introduce new iPhone software that uses artificial intelligence (AI) to detect and report child sexual abuse.  That action by Apple raises several personal privacy questions including:

  • How much personal privacy is one willing to give up trying to halt this abhorrent behavior?
  • How much do we trust the organization (Apple in this case) in their use of the data to stop child pornography?
  • How much do we trust that the results of the analysis won’t get into unethical players’ hands and used for nefarious purposes?

In particular, let’s be sure that we have thoroughly vented the costs associated with the AI model’s False Positives (accusing an innocent person of child pornography) and False Negatives (missing people who are guilty of child pornography). That is the focus of Part 2!

AI literacy starts by understanding how an AI model works (See Figure 1).

Figure 1: “Why Utility Determination Is Critical to Defining AI Success

AI models learn through the following process:

  1. The AI Engineer (in very close collaboration with the business stakeholders) defines the AI Utility Function, which are the KPIs against which the AI model’s progress and success will be measured.
  2. The AI model operates and interacts within its environment using the AI Utility Function to gain feedback in order to continuously learn and adapt its performance (using backpropagation and stochastic gradient descent to constantly tweak the models weights and biases).
  3. The AI model seeks to make the “right” or optimal decisions, as framed by the AI Utility Function, as the AI model interacts with its environment.

Bottom-line: the AI model seeks to maximize “rewards” based upon the definitions of “value” as articulated in the AI Utility Function (Figure 3).

Figure 2:  “Will AI Force Humans to Become More Human?”

To create a rational AI model that understands how to make the appropriate decisions, the AI programmer must collaborate with a…

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