@article{1439713,
      recid = {1439713},
      author = {Agarwal, Sray, and Mishra, Shashin,},
      title = {Responsible AI : implementing ethical and unbiased  algorithms /},
      pages = {1 online resource (xix, 177 pages) :},
      abstract = {This book is written for software product teams that use  AI to add intelligent models to their products or are  planning to use it. As AI adoption grows, it is becoming  important that all AI driven products can demonstrate they  are not introducing any bias to the AI-based decisions they  are making, as well as reducing any pre-existing bias or  discrimination. The responsibility to ensure that the AI  models are ethical and make responsible decisions does not  lie with the data scientists alone. The product owners and  the business analysts are as important in ensuring  bias-free AI as the data scientists on the team. This book  addresses the part that these roles play in building a  fair, explainable and accountable model, along with  ensuring model and data privacy. Each chapter covers the  fundamentals for the topic and then goes deep into the  subject matter--providing the details that enable the  business analysts and the data scientists to implement  these fundamentals. AI research is one of the most active  and growing areas of computer science and statistics. This  book includes an overview of the many techniques that draw  from the research or are created by combining different  research outputs. Some of the techniques from relevant and  popular libraries are covered, but deliberately not drawn  very heavily from as they are already well documented, and  new research is likely to replace some of it. Hands-on  approach to ensure easy practical implementation of the  concepts discussed Most of the techniques covered are new,  with only a few that refer to existing packages. For the  techniques covered, the book goes deep into the subject  matter and includes code to help the product teams  implement these techniques for their products Also  addresses the contribution that product owners and the  business analysts make to the product being fair and  explainable, explaining every topic in detail, including  the math involved Covers the end-to-end view of what any  software product team needs to do to be able to create a  robust, successful and fair AI-driven product Most of the  chapters include notes sections throughout to cover the  topic in progress for all audiences. Non-technical readers  will also benefit by the introductions and conclusions for  the book and in each of the chapters.},
      url = {http://library.usi.edu/record/1439713},
      doi = {https://doi.org/10.1007/978-3-030-76860-7},
}