Preface

Acknowledgements

Author biography

1 Preamble

2 Discrimination, bias, and fairness

3 Algorithmic decision making

4 Content Governance

5 Interpretable versus explainable models

6 Newly emerging paradigms

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. Some of the glaring questions of current times are: will AI systems replace humans in various job sectors? How can we tackle the problem of biased learning in AI behaviour; for instance, reinforcing racial and gender informed discriminations in human decision making. How do we allocate responsibility for accidents/errors caused by AI systems such as autonomous vehicles or medical diagnostics? Can we have AI systems that complement rather than displace human intelligence to support a more sustainable and just world?

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. The main aim is to prepare the reader to understand the critical capacity of the rapidly evolving AI technology and its societal implications.

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.