Call for Chapters: Enhancing Automated Decision-Making Through AI

Editors

Shalin Hai-Jew, Hutchinson Community College, United States

Call for Chapters

Proposals Submission Deadline: May 12, 2024
Full Chapters Due: August 25, 2024
Submission Date: August 25, 2024

Introduction

Computational capabilities bring with it the advantages of cold logic, precision, speed, data omniscience, mass data processing capabilities, multimodality, enriched deployments, and efficiencies. With these many advantages, people seek to offload work to machines. Now with the major advances in artificial intelligence, humanity is moving closer to handing over complex decision-making to machines, without humans in the direct loop. While the respective rationales for the handover to automation tend to be for positive ends, the outcomes have not always been so. Automated safety systems have resulted in catastrophic all-fatal crashes of jetliners. Mis-codings have resulted in lost rockets and satellites. As automated decision-making is embedded in more systems, the risks accrue. This work explores the processes of designing and deploying systems for automated decision-making. How are systems designed to anticipate all eventualities? What machine “objectivity” and “neutrality” may be designed and actualized? What do the testing regimens look like at full rigor? What are some of the potential outcomes? When AI generates decisions in situ, what does that look like? What are the enablements vs. constraints? AI, in this current phase of development, is volatile and unpredictable and powerful. This collection also asks: What decisions are humans willing to delegate to machines, and why? This is a relevant question especially given that humans take all consequences, come what may. Everyone is getting into the game. This work involves various contexts: work-based ones (industry, business, education, healthcare, communications); public works (transportation, smart cities); military, and others. Where should humans be disintermediated, and where should they not be? Who/what should be at the controls at any particular slice-in-time, and why? How can humanity avoid "solutions" that may make problems worse? Certainly, “first, do no harm” is an important approach, to avoid triggering events, irrecoverable and irredeemable moments, cascading failures. What these may be have not been fully explored at present.

Objective

This edited book is an elicitation to professionals working in this space to consider the implications of automated decision-making informed by AI, which has unpredictability and wildcard factors. The question is, “How can such powerful technologies be harnessed in ways that benefit humanity writ large, at meso levels, and at the individual levels?” What are ways to create, test, and deploy such systems for efficacy? In this early moment, even applied “thought experiment” types of works are invited. This book is about throwing a conversation about important topics in this space. Various applied areas may be considered: smart cities, surveillance, transportation, law enforcement, adjudication; business; education; agriculture; healthcare; design; engineering; entertainment, media; law; and others.

Target Audience

This work is for engineers, systems designers, instructors, graduate students, and others working in this space.

Recommended Topics

Particular “use cases” of AI-informed decision-making (with humans in the loop, without humans in the loop)

Thought experiments in AI decision-making

Preventing “AI remorse”

Avoiding “AI determinism”

Preventing malicious human usage of AI in automation

Decision flows

Decision sequences

Critical decision junctures

Escalatory and de-escalatory ladders

Sequence breaks and where

Controlling for how computers solve for desired outcomes (while exploiting their creativity)

Risks of mysteries in hidden machine-based “computational thinking”

A lurid imagination for risk (to aid risk mitigation)

Anticipating hyper-rare events

Modeling reality

Sensors and data sources

Indicators

Creativity in generative AI

Evolution of machine capabilities (through learning)

Training and programming with data, visuals, multimedia, text

Fine-tuning systems with directive code, algorithms

General intelligence and beyond

What can be prepared for, what are wildcard factors (and consequential mixes of possibilities)

Troubleshooting AI-informed decision-making technologies

Automated decision-making with AI

Addressing limits to automated decision-making

Encoding human values, laws, and norms to positive effect

Ethics in AI (and computation) in automated decision-making

Legal considerations in AI (and computation) in automated decision-making

Cautionary tales of computational decision-making from life

Assessing AI abilities and judgments in automated decision making; testing

Wildcard factors in computation decision-making

Building safeguards into automated decision-making sequences

Validating indicators (and protecting against deceptions and hacks) in automated decision-making sequences

Computational agentry in human stand-ins

Controlling for unintended effects and amplifications

Monitoring for machine-based instigatory actions on vulnerable (susceptible) people

Defining context through computational means

Overrides to machine decision-making; overrides to human decision-making

Human vs. machine in decision-making

Assessing human vs. machine in decision-making

Co-decision-making for efficacies

Conceptualizing “edge cases” to explore potentialities and to design against unnecessary risks

Dynamic world adaptability (with novel circumstances)

Designing reward functions



Submission Procedure

Researchers and practitioners are invited to submit on or before May 12, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by May 26, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by August 25, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Enhancing Automated Decision-Making Through AI. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

May 12, 2024: Proposal Submission Deadline
May 26, 2024: Notification of Acceptance
August 25, 2024: Full Chapter Submission
October 27, 2024: Review Results Returned
December 8, 2024: Final Acceptance Notification
December 22, 2024: Final Chapter Submission



Inquiries

Dr. Shalin Hai-Jew
Hutchinson Community College
haijes@gmail.com



Classifications


Business and Management; Computer Science and Information Technology; Education; Science and Engineering
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