Reading Guide: Advances in AI, Software and Systems Engineering (AHFE 2020)

Most AI books ask what a system can do. The Applied Human Factors and Ergonomics (AHFE) community asks a different question: how do these systems work for the people who build, use and live with them? Advances in Artificial Intelligence, Software and Systems Engineering, edited by Tareq Ahram and published by Springer as volume 1213 of the Advances in Intelligent Systems and Computing series, collects papers from two AHFE 2020 virtual conferences held July 16 to 20, 2020: one on software and systems engineering, and one on artificial intelligence and social computing. This guide shows you how to navigate its roughly 615 pages.

Get the book: View Advances in AI, Software and Systems Engineering (AHFE 2020) on Amazon

What this book is (and who it's for)

This is a conference proceedings volume: a collection of short, peer-reviewed papers rather than a single narrative. The two source conferences were the 7th International Conference on Human Factors, Software and Systems Engineering and the 3rd International Conference on Human Factors in Artificial Intelligence and Social Computing. Library catalogs list its subjects as computational intelligence, machine learning, cyber-physical systems and the Internet of Things, and embedded systems.

The common thread is people. Expect papers that look at AI and software through the lens of usability, trust, collaboration, workload and real-world deployment. It suits:

• Human factors and HCI researchers who want to see how their field is engaging with AI.
• Software and systems engineers interested in user-centered design of complex systems.
• Graduate students looking for applied, interdisciplinary research topics.

Some familiarity with basic machine learning concepts and with software development processes is helpful, but the papers are generally applied and approachable compared with purely theoretical AI venues.

Before you start

• Remember the date. These papers were presented in mid-2020, before the wave of large language models. Read them as a snapshot of that moment and check newer work before relying on any finding.
• Set up a simple tracker. For each paper, record its research question, method (survey, experiment, case study, prototype), setting and main takeaway.
• Use the author index. The volume includes one, which helps when you want to follow a particular research group's work.

How to read it

Because the volume combines two conferences, the easiest approach is to read by track and theme rather than front to back.

Pass 1: Map the volume

Skim the table of contents and read abstracts only. Sort papers into two piles that match the source conferences: AI and social computing, and software and systems engineering. Within each pile, group papers by theme, for example trust and explainability, human-AI teaming, applied machine learning, software process and tools, and systems applications.

Pass 2: Choose a track

• If your interest is AI and society: start in the AI and social computing papers. Look first for work on trust, acceptance and decision support, because those themes connect most directly to human factors.
• If you are a software or systems engineer: start with the software and systems engineering papers, focusing on case studies and applied tools you could adapt in your own projects.
• If you want a research topic: look for papers that bridge both tracks, such as AI-assisted engineering tools or user studies of intelligent systems. Interdisciplinary gaps are fertile ground.

Pass 3: Go deep on a few

Select three to five papers and read them closely. Pay attention to sample sizes, study settings and stated limitations, which vary widely in short conference papers.

What to skim on a first pass

Skip detailed implementation descriptions and long results tables until you've decided a paper is worth reproducing or citing.

Key ideas to take away

• Human factors are an engineering requirement. A technically strong AI or software system can still fail if people don't trust, understand or adopt it.
• Socio-technical thinking. Systems include the people, organizations and processes around the code, and good design accounts for all of them.
• Applied research has its own rigor. Case studies and user studies offer insight that benchmarks can't, but they need careful reading of context.
• Timing matters. These 2020 papers provide a baseline for how the conversation about human-centered AI has since evolved.

Practice and discussion questions

1. Choose one AI and social computing paper. How did it measure trust or acceptance, and is that measure convincing?
2. Pick a software engineering paper. Could its approach be applied on a team you know? What would get in the way?
3. What does "human factors" add to the design of an AI system that a pure performance metric misses?
4. Find a paper that would look different if written today, after large language models went mainstream. What would change?
5. Which paper offers the most practical, immediately usable idea? Why?
6. How would you design a small study to extend one paper's findings?

Pair it with

• Human-centered AI in industry. AHFE's general chair, Waldemar Karwowski, co-edited Advances in Artificial Intelligence Applications in Industrial and Systems Engineering (Wiley), which we also carry and which has its own reading guide.
• Applied AI proceedings. For a different conference's view of applied AI, see Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2019), also in our store.
• Usability and user research methods. An introduction to HCI evaluation methods will help you judge the user studies in this volume.

Keep going

This volume rewards readers who care about how technology meets people. Map the two tracks, pick the one that fits you, and go deep on a handful of papers, and you'll come away with a richer, more human view of AI and software engineering. Get Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2020) on Amazon, or browse our bookshelf for more.

As an Amazon Associate, BooksPlusAi may earn from qualifying purchases.

Sources

• TUM Library catalog record (AISC 1213)
• CiNii Books record
• we:kb title record
• AHFE 2020 Final Program (PDF)