Ethics in Artificial Intelligence: Bias, Fairness and Beyond
Mukherjee, Animesh, Kulshrestha, Juhi, Chakraborty, Abhijnan
相關主題
商品描述
This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments - the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in softwaretesting/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.
商品描述(中文翻譯)
這本書是關於人工智慧倫理的新興領域的章節集合。該書包含了該領域的領先專家撰寫的章節，使其成為一個獨特的集合。該書的一些重要特點包括結合了關於將倫理融入人工智慧的理論和實踐方面的章節。該書觸及了該領域的所有重要概念，包括偏見、歧視、公平性和可解釋性。整體組成可以大致分為兩個部分 - 第一部分包括在影響評估、廣告和個性化、計算社會科學和信息檢索中實證識別偏見、歧視和相關的倫理問題。第二部分包括將公平性的概念操作化，識別在分配、聚類和時間序列問題中公平性的重要性，以及在軟件測試/調試和多利益相關者平台中應用公平性。該部分以一章關於機器學習模型的可解釋性結束，這是該領域中另一個非常重要且新興的主題。
作者簡介
Animesh Mukherjee is an Associate Professor and the A. K. Singh Chair at the Department of Computer Science and Engineering, IIT Kharagpur, West Bengal, India. His main research interests center around investigating hate and abusive content on social media platforms, fairness, bias in information retrieval systems, media bias, and quality monitoring of Wikipedia articles. He publishes and is on the committee of most of the top AI conferences, including The Web Conference, NeurIPS, AAAI, IJCAI, ACL, EMNLP, NAACL, Coling, CSCW, ICWSM, etc.
Juhi Kulshrestha is an Assistant Professor at the Department of Computer Science at Aalto University, Finland. She obtained a doctoral degree from the Max Planck Institute for Software Systems, Germany. She also pursued research at the University of Konstanz and Leibniz Institutes for Social Sciences and Media Research. Her research, at the intersection of computer science and social science, broadly focuses on leveraging digital behavioral data to quantify and characterize how people consume news and information on the web and its effect on society. She regularly publishes in and serves on the committees of top-tier venues such as TheWebConf, AAAI ICWSM, ACM CSCW, and ACM FAccT. She is a recipient of several internationally competitive research grants such as Meta´s Foundational Integrity Research Grant, Social Science One´s Social Media and Democracy Research Grant, Google European Doctoral Fellowship for Social Computing, and Google Anita Borg Scholarship.
Abhijnan Chakraborty is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Delhi. He is also associated with the School of Artificial Intelligence and the School of Information Technology at IIT Delhi. His research interests fall under the broad theme of Computing and Society, covering the research areas of Social Computing, Information Retrieval and Fairness in Machine Learning. In the past, he has worked at the Max Planck Institute for Software Systems, Germany and Microsoft Research India. During PhD, he was awarded the Google India PhD Fellowship and the Prime Minister's Fellowship for Doctoral Research. He regularly publishes in top-tier computer science conferences including WWW, KDD, AAAI, AAMAS, CSCW and ICWSM. He has won INAE Young Engineer 2022 award, the best paper award at ASONAM'16 and best poster award at ECIR'19. He is one of the recipients of an internationally competitive research grant from the
Data Transparency Lab to advance his research on fairness and transparency in algorithmic systems.
Srijan Kumar is an Assistant Professor at the School of Computational Science and Engineering, College of Computing at the Georgia Institute of Technology. He completed his postdoctoral training at Stanford University, received a Ph.D. and M.S. in Computer Sciencefrom the University of Maryland, College Park, and B.Tech. from the Indian Institute of Technology, Kharagpur. He develops Data Mining methods to detect and mitigate the pressing threats posed by malicious actors (e.g., evaders, sockpuppets, etc.) and harmful content (e.g., misinformation, hate speech etc.) to web users and platforms. He has been selected as a Kavli Fellow by the National Academy of Sciences, named as Forbes 30 under 30 honoree in Science, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, and best paper honorable mention award from the ACM Web Conference. His research has been covered in the popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine.
作者簡介(中文翻譯)
Animesh Mukherjee是印度西孟加拉邦卡拉格普爾印度理工學院計算機科學與工程系的副教授和A. K. Singh講座教授。他的主要研究興趣集中在調查社交媒體平台上的仇恨和辱罵內容、信息檢索系統中的公平性和偏見、媒體偏見以及對維基百科文章的質量監控。他發表論文並參與了大多數頂級人工智能會議的委員會，包括The Web Conference、NeurIPS、AAAI、IJCAI、ACL、EMNLP、NAACL、Coling、CSCW、ICWSM等。
Juhi Kulshrestha是芬蘭阿爾托大學計算機科學系的助理教授。她在德國馬克斯普朗克軟件系統研究所獲得博士學位。她還在康斯坦茨大學和萊布尼茲社會科學和媒體研究所進行研究。她的研究涉及計算機科學和社會科學的交叉領域，主要關注利用數字行為數據量化和描述人們在網絡上如何消費新聞和信息以及其對社會的影響。她定期在TheWebConf、AAAI ICWSM、ACM CSCW和ACM FAccT等頂級會議上發表論文並擔任委員會成員。她獲得了多個國際競爭性研究資助，如Meta的基礎完整性研究資助、Social Science One的社交媒體和民主研究資助、Google歐洲社交計算博士獎學金和Google Anita Borg獎學金。
Abhijnan Chakraborty是印度德里印度理工學院計算機科學與工程系的助理教授。他還與印度理工學院德里分校的人工智能學院和信息技術學院有關。他的研究興趣涵蓋計算機與社會的廣泛主題，包括社交計算、信息檢索和機器學習中的公平性。過去，他曾在德國馬克斯普朗克軟件系統研究所和微軟印度研究院工作。在攻讀博士學位期間，他獲得了Google印度博士獎學金和總理博士研究獎學金。他定期在WWW、KDD、AAAI、AAMAS、CSCW和ICWSM等頂級計算機科學會議上發表論文。他獲得了INAE Young Engineer 2022獎、ASONAM'16最佳論文獎和ECIR'19最佳海報獎。他是Data Transparency Lab的國際競爭性研究資助的受獎者，該資助旨在推進他對算法系統公平性和透明度的研究。
Srijan Kumar是喬治亞理工學院計算科學與工程學院計算科學與工程學院的助理教授。他在斯坦福大學完成了博士後培訓，並在馬里蘭大學學院公園獲得了計算機科學的博士和碩士學位，以及印度理工學院卡拉格普爾分校的學士學位。他開發數據挖掘方法來檢測和緩解惡意行為者（例如逃避者、假人等）和有害內容（例如錯誤信息、仇恨言論等）對網絡用戶和平台的威脅。他被美國國家科學院選為Kavli Fellow，被福布斯評選為科學領域的30位30歲以下榮譽人士，並獲得了ACM SIGKDD博士論文獎2018的亞軍和ACM Web Conference的最佳論文榮譽提名獎。他的研究受到CNN、華爾街日報、Wired和紐約雜誌等媒體的報導。
