Decision Intelligence: Human–Machine Integration for Decision-Making

Description

Revealing the limitations of human decision-making, this book explores how Artificial Intelligence (AI) can be used to optimize decisions for improved business outcomes and efficiency, as well as looking ahead to the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise. From the theories and concepts used to design autonomous intelligent agents to the technologies that power DI systems and the ways in which companies use decision-making building blocks to build DI solutions that enable businesses to democratize AI, this book presents an impressive framework to integrate artificial and human intelligence for the success of different types of business decisions. Replete with case studies on DI applications, as well as wider discussions on the social implications of the technology, Decision Intelligence: Human–Machine Integration for Decision Making appeals to both students of AI and data sciences and businesses considering DI adoption. Cover Half Title Title Page Copyright Page Contents List of Acronyms Preface Acknowledgments Chapter 1. Decision Intelligence: Introduction and Overview INTRODUCTION TO DI Defining Decision Intelligence DI Evolution and Landscape WHY WE NEED DI DI to Optimize Decisions DI for Improved Business Outcomes and Efficiency HOW DI WORKS AND HOW IT LOOKS Types of Business Decisions Decision-Making Process DI Forms Decision Assistance Decision Support Decision Augmentation Decision Automation Infrastructure Design – Data Architecture for DI STATE OF DI ADOPTION Factors Affecting DI Adoption Decisions CONCLUSION CASE STUDY: AI-POWERED RECOMMENDATION SYSTEM DELIVERING CONSISTENT ENERGY SAVING AT GOOGLE DATA CENTERS QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 2. Humans vs. Machines in Decision-Making HUMANS IN DECISION-MAKING Behavioral Economics of Decision-Making Neuroscience and Neuroeconomics Perspectives COMPUTERS IN DECISION-MAKING Basic Programming Methods The Evolution of AI-Powered Decision-Making Machine Learning Supervised Machine Learning Unsupervised Machine Learning Reinforcement Learning Classical Machine Learning Neural Networks and Deep Learning HUMAN VS. COMPUTER – WHO IS BETTER AT DECISION-MAKING? CONCLUSION CASE STUDY: JOHNS HOPKINS MANAGES PATIENT FLOW DURING COVID-19 WITH AI-POWERED CAPACITY COMMAND CENTER QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 3. Systems and Technologies for Decision-Making ORGANIZATION AS A SYSTEM DECISION-MAKING SYSTEM IN THE ORGANIZATION Decision-Making Environments Human Agents SUPPORTING TECHNOLOGIES FOR MODERN DI SYSTEMS AutoML Computer Vision Audio Processing Natural Language Processing (NLP) TECHNOLOGICAL SYSTEMS FOR DECISION-MAKING Decision Support Systems Intelligent Agents KINDS OF INTELLIGENT AGENTS Recommender Systems CONCLUSION CASE STUDY: RECOMMENDER SYSTEM FOR COVID-19 RESEARCH – INNOVATIVE DEEP NEURAL NETWORK MODELS QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 4. Intelligent Agents: Theoretical Foundations MULTIDISCIPLINARITY OF INTELLIGENT AGENTS AGENTS FOR SIMPLE DECISIONS Decision Networks Calculating Utilities to Determine the Optimal Decision Expected Utilities of the Two Decisions and MEU The Value of Information Expected Utilities of the Two Decisions and MEU AGENTS FOR COMPLEX DECISIONS Dynamic Decision Networks Solving MDPs with Value Iteration and Policy Iteration Value Iteration Policy Iteration Monte Carlo Methods MULTIAGENT DECISION-MAKING Pure Strategy and Saddle Point Equilibrium Mixed Strategy and Nash Equilibrium Dominant Strategy Equilibrium Pareto-Optimal Outcome CONCLUSION CASE STUDY: DESIGNING AGENT FOR COMPLEX ENVIRONMENT – MULTIAGENT PATH PLANNING WITH NONLINEAR MODEL PREDICTIVE CONTROL QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 5. Decision-Making Building Blocks, Tools, and Techniques DATA FOR DECISION-MAKING DECISION ANALYSIS Decision Tables Decision Trees DECISION MODELING Predictive Modeling Regression Models Classification Models Time Series Models Outliers Models Clustering Models Prescriptive Modeling Heuristics Models Optimization Models LINEAR PROGRAMMING OPTIMIZATION Simulation Models TEXT ANALYTICS TECHNIQUES FOR DECISION-MAKING CONCLUSION CASE STUDY: DETECTING ANOMALIES AND PREVENTING EQUIPMENT FAILURES IN STEEL WITH NOODLE.AI ASSET FLOW QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 6. Decision Intelligence Market: Vendors and Solutions DI SOLUTIONS DI VENDORS Peak Tellius Xylem Noodle.ai Aera Technology Diwo Quantellia CONCLUSION CASE STUDY: SISU HELPS SAMSUNG JUMPSTART A $1 BILLION PRODUCT LAUNCH QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 7. Decision Intelligence Framework for Organizational Decision-Making WHY WE NEED A FRAMEWORK FOR DECISION-MAKING Deciding How to Decide DI FRAMEWORK Preparation and Planning The Seven-Step Process Step 1: Setting Key Goals Step 2: Defining the Decision Step 3: Rating the Decisions on Importance and Complexity Levels Step 4: Prioritizing and Classifying Decisions to Determine the PI-AI Mix Step 5: Formulating Decision Implementation Strategy Step 6: Implementing the Strategy Step 7: Evaluating the Strategy CONCLUSION CASE STUDY: DRÄGER IMPROVES CUSTOMER SERVICE WITH STARMIND – LESS TIME SEARCHING, MORE TIME FOR CUSTOMERS QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES Chapter 8. Recommendations for DI Implementation and Ethics RECOMMENDATIONS FOR DI IMPLEMENTATION Start Early, Start Small Develop a Business Case Make It a Strategic Initiative Use Project Management Approach Build Capacity, Capabilities, and Infrastructure DI Education and Training Partner for Success Use Design Thinking Focus on Collaboration Build a DI Culture Monitor Performance, Assess Impact, and Modify DI Solutions DI READINESS ASSESSMENT Strategic and Leadership Readiness Infrastructural and Operational Readiness Talent and Cultural Readiness DI Readiness Audit ETHICS FOR DI Biased Algorithms Data Privacy and Protection Accuracy of Data and Information Job Loss INITIATIVES OF LARGE CORPORATIONS TO PROMOTE AI ETHICS CONCLUSION AND THE FUTURE OF DI CASE STUDY: AI FOR GREATER GOOD – STANFORD MEDICINE USES GOOGLE GLASS TO HELP KIDS WITH AUTISM SOCIALIZE QUESTIONS QUESTIONS FOR DISCUSSION REFERENCES INDEX