Collaborative Agency of Artificial Intelligence: Human and Machine

Most of us think of AI as new technology working on its own – think self-driving cars, robots in factories, and chatbots like Siri and Alexa.  The truth is that Artificial Intelligence, done by computers, is both created and monitored by human beings like us; no robot works in a vacuum.  Robots and humans collaborate in tandem because digital systems are key to organizations’ success. 

There are two parts to this collaboration: humans with machines and humans with one another on teams. Let’s look at the part one: humans with machines presented in chapter one of The Digital Mindset. [1] Paul Leonardi and Tsedal Neeley begin with the insistence that we never treat machines as other than machine.

As robots act like humans, we tend to treat them as human; that’s when trouble begins. We need to understand how artificial intelligence works, how machines learn, how to stack technology, and how to treat AI as a machine. A digital mindset shifts how we think about our relationship to machines with explicit instructions on narrow tasks. 

The three basic components that computer scientists use to develop AI are data, processing power, and algorithms. We view AI as futuristic, something coming; but it’s here now. We use it more than we know. We begin with how AI works. Put these steps together and we can guess how computers do AI. 

Data (pronounced day-ta = long ā = dāta) is gathered into categories and “cleaned” by statistics and classified.  A data scientist further processes data into patterns sorted and converted into algorithms, instructions directing the computer to complete an assigned task.  Simply, a computer processes data from statistically classified data that an algorithm tells a computer to do.  In mathematical terms, data is the “input” of information into an algorithm that “outputs” results such as a decision. The “black box” is what the algorithm is doing between “input and output.”

When we talk about machine learning, we mean computer learning; a computer is a machine, and it works with mathematical numbers of 0s and 1s that make up files in sizes of clusters of 0s/1s in quantities of kilobyte, megabyte, etc. Computer/machine learning is done by millions of “exposures” to objects, say a dog – all kinds. The machine never understands what a dog is but knows one when it sees one – “supervised learning” – step one.

 The machine continues learning by algorithms monitored by data experts in “deep learning” when algorithms prompt the machine to process larger data to find new kinds of dogs on its own – “unsupervised learning” – step two.  The third step of computer/machine learning is when the algorithm helps the machine process its own mistakes by corrections through what is termed “reinforced learning.”  This is automation without consciousness in the machine/computer.

Our final look into machine learning is stacking of technology in data processing. Here we see two subsystems: the “front end,” the client side (that’s us sitting at a computer screen with its icons and buttons), and the “back end,” the “server side” where all the processing takes place – what the software is doing mechanically to make the screen shine.

The second part of collaboration agency happens when humans work on teams. Our book, The Digital Mindset, explains in chapter three that team members cultivate your digital presence.

Developing a digital mindset means learning to stay in sync with remote collaborators. … Mutual knowledge is the information we need to achieve through mutual understanding. It is the common ground that people use to reach through mutual knowledge, collaboration, and team digital presence. 

At the end of chapter two, Getting to 30 Percent, our authors summarize the challenge of digital presence by outlining steps to maintain digital presence.  This involves sending updates on progress without needing an immediate response, using a little ambiguity/curiosity when wanting a response, being timely on developments of projects, always show purpose and willingness to learn, focus on the right data free of distraction, and don’t be afraid to be personal about socializing once-in-awhile via media platforms.  These suggestions close with “Remain in mind when out of sight.” Collaboration is the name of the game.


[1]  Leonardi, Paul & Neeley, Tsedal (2022); The Digital Mindset: What it Really Means to Thrive in the Age of Data, Algorithms, and AI, Harvard Business School Publishing, Boston, MA