"Version: 20230701"--Title page verso.

Includes bibliographical references.

1. Preamble -- 1.1. What is ethics? -- 1.2. What is AI? -- 1.3. What is AI and ethics? -- 1.4. What is a computational perspective?

2. Discrimination, bias, and fairness -- 2.1. Chapter foreword -- 2.2. Sensitive attributes -- 2.3. Notions of discrimination -- 2.4. Types of bias -- 2.5. Operationalizing fairness -- 2.6. Relationships between fairness metrics -- 2.7. Individual versus group fairness

3. Algorithmic decision making -- 3.1. Chapter foreword -- 3.2. The ProPublica report -- 3.3. Gender shades -- 3.4. Fair ML

4. Content governance -- 4.1. Chapter foreword -- 4.2. Content dissemination -- 4.3. Content moderation

5. Interpretable versus explainable models -- 5.1. Chapter foreword -- 5.2. What is interpretability? -- 5.3. What is explainability? -- 5.4. Operationalizing interpretability -- 5.5. Operationalizing explainability

6. Newly emerging paradigms -- 6.1. Chapter foreword -- 6.2. Ethical use of AI for law enforcement -- 6.3. Data protection and user privacy -- 6.4. Moral machines -- 6.5. Singularity and ethics.

Developed from a graduate course this book examines some of the deep issues that have become very relevant due to the increasing use of AI in recent times. The book discusses various aspects ranging from the effects of proliferation of algorithmic decision making, autonomous systems, explainability in machine learning, the question of balance between regulation and innovation, the adversarial role of AI in information dissemination and the questions of individual rights, fairness and discrimination. Taking a computational approach, rather than one dominated by philosophical or legal discussions, this book is geared toward increasing awareness of the immediate moral and legal repercussions of the presence and possibly dominance of AI in our society. Part of IOP Series in Next Generation Computing.

Students and researchers from subjects such as computing, mathematics and the sciences.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Presently, Animesh Mukherjee is a Full Professor in the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur. He is also a distinguished Member of ACM. His main research interests centre around content governance which includes: (i) content moderation (harmful content analysis, detection, and mitigation); (ii) content dissemination (fairness issues in e-commerce platforms and interfaced systems like facial recognition, automatic speech recognition systems etc); and (iii) content maintenance (quality analysis and improvement of encyclopaedias like Wikipedia and large software systems like Ubuntu releases). He regularly publishes in all top CS conferences including AAAI, IJCAI, ACL, NAACL, EMNLP, The Web Conference, CSCW etc. He has received many notable awards and fellowships including the Facebook ethics for AI research award, India, Google course award for the course AI and Ethics, IBM faculty award, Humboldt Fellowship for Experienced Researchers, Simons Associateship, ICTP to name a few.

Title from PDF title page (viewed on August 1, 2023).