Artificial Intelligence (AI) for Business Librarians

This guide is designed for BRASS members and others interested in business librarianship as an introduction to AI and source of useful resources on the topic. Topics covered include information literacy, using AI effectively in academia and business, evaluating outcomes, ethics of AI, and useful resources.

Part of the excitement around AI is that in recent years these technologies have outperformed even their creators' expectations. AI works by finding patterns in troves of data, and then matching the found patterns to some desired output. Most AI systems are also the result of human intervention to help further guide the output towards desired goals.

Though the underlying systems may be complicated, it’s important to understand that AI is dependent on rich and varied troves of data on a relevant topic for the technology to work. The quality of the data that is used is crucial in the ability of the technology to deliver useful results. Weaknesses in the data, such as biased or inexpert sources, will give weak results. Other issues of appropriateness of data usage that will be familiar to librarians are also relevant here (copyright, recency of publication, etc.).

Artificial Intelligence - Artificial intelligence is a machine’s ability to perform the cognitive functions we usually associate with human minds. (McKinsey)

Human in the Loop—"Human-in-the-loop is a branch of AI that brings together AI and human intelligence to create machine learning models. It’s when humans are involved with setting up the systems, tuning and testing the model so the decision-making improves, and then actioning the decisions it suggests. “(Faculty.ai)
Deep Learning – “[Deep Learning] involves passing data through webs of math loosely inspired by the working of brain cells that are known as artificial neural networks. As a network processes training data, connections between the parts of the network adjust, building up an ability to interpret future data.”(Wired)

Generative AI – “Generative AI is a catch-all term for AI that can cobble together bits and pieces from the digital world to make something new—well, new-ish—such as art, illustrations, images, complete and functional code, and tranches of text that pass not only the Turing test, but MBA exams. Tools such as OpenAI’s Chat-GPT text generator and Stable Diffusion’s text-to-image maker manage this by sucking up unbelievable amounts of data, analyzing the patterns using neural networks, and regurgitating it in sensible ways.”(Wired)

Hallucinations—"AI hallucinations are incorrect or misleading results that AI models generate. These errors can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.”(Google Cloud)

Large Language Models—"A language model is a machine learning model that aims to predict and generate plausible language. Autocomplete is a language model, for example. These models work by estimating the probability of a token or sequence of tokens occurring within a longer sequence of tokens.” (Google)

Machine Learning – “Machine Learning (ML) is the part of AI studying how computer agents can improve their perception, knowledge, thinking, or actions based on experience or data. For this, ML draws from computer science, statistics, psychology, neuroscience, economics and control theory”(Stanford Human-Centered AI)

Prompt Engineering – “Prompt engineering is the practice of designing inputs for AI tools that will produce optimal outputs.”(McKinsey)

Faculty.ai (2021, December 1) What is 'human-in-the-loop? And why is it more important than ever? What is 'human-in-the-loop'? https://faculty.ai/blog/what-is-human-in-the-loop/

Google Cloud ( What are AI hallucinations? What are AI hallucinations | Google Cloud. https://cloud.google.com/discover/what-are-ai-hallucinations

McKinsey & Company. (2023, April 24). What is ai?. What is AI (Artificial Intelligence)? https://www.mckinsey.com/featured insights/mckinsey explainers/what-is-ai