Preface & acknowledgments51Introduction: choose for AI and for explainability82Systems that explain themselves have a better ROI122.1Explanations make a solution easier adaptable to change132.2Explanations increase the success rate of AI solutions133Your business will be more trusted163.1Making credible decisions163.2Being a responsible business164Explanations enhance decision making184.1Unwanted decision biases come to the surface204.2Missing commonsense knowledge is detected205Explainable AI is feasible using a six-step approach225.1Get a shared understanding of the domain225.2Understand the task and select the right scope245.3Collect the right data and improve its quality255.4Select AI techniques that deliver results265.5Generate good explanations295.6Evolve the system over time316Society is demanding transparency346.1People have ethical concerns346.2Governments prepare legislation demanding explanations.367Terminology388Recommendations for further reading and sources used.40Making predictions and good decisions is difficult. Huge amounts of data are available, and it promises a new source for knowledge to improve human decision making. However, the available technology is not transparent, the resulting decisions may be biased, and people question the ethics of AI. This book offers the solution: explainable (XAI) AI. It provides 5 reasons why explainable AI is more successful and six steps to start successfully applying XAI.In AIX, an acknowledged expert in the field of AI provides practical guidance and convincing arguments for responsible business managers to take ownership of the intelligence required to design effective decision support systems. Easy to read, using many examples and by demystifying AI technology this book will replace fear for AI with a simple and responsible strategy. Insightful for managers and specialists.