Embedding ethics in AI:
principles, practice, technical solutions, and challenges
Francesca Rossi | IBM fellow and AI Ethics Global Leader
Abstract:
AI is going to bring huge benefits in terms of scientific progress, human wellbeing, economic value, and the possibility of finding solutions to major social and environmental problems. Supported by AI, we will be able to make more grounded decisions and to focus on the main values and goals of a decision process rather than on routine and repetitive tasks. However, such a powerful technology also raises some concerns, related for example to the black-box nature of some AI approaches, the possible discriminatory decisions that AI algorithms may recommend, and the accountability and responsibility when an AI system is involved in an undesirable outcome. Also, since many successful AI techniques rely on huge amounts of data, it is important to know how data are handled by AI systems and by those who produce them. These concerns are among the obstacles that hold AI back or that cause worry for current AI users, adopters, and policy makers. Without answers to these questions, many will not trust AI, and therefore will not fully adopt it nor get its positive impact.
In this talk I will present the main issues around AI ethics, describe some technical solutions in the recommender system area, mention some outstanding challenges, and propose a possible way to address them.
Short Bio
Francesca Rossi is an IBM fellow and the IBM AI Ethics Global Leader.
She is based at the T.J. Watson IBM Research Lab, New York, USA.
She has been a professor of computer science at the University of Padova for 20 years before joining IBM.
Her research interests focus on artificial intelligence, specifically they include constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues in the development and behavior of AI systems, in particular for decision support systems for group decision making. On these topics, she has published over 200 scientific articles in journals and conference proceedings, and as book chapters.
She is a fellow of both the worldwide association of AI (AAAI) and of the European one (EurAI).
She has been president of IJCAI (International Joint Conference on AI), an executive councillor of AAAI, and the Editor in Chief of the Journal of AI Research. She is a member of the scientific advisory board of the Future of Life Institute (Cambridge, USA) and a deputy director of the Leverhulme Centre for the Future of Intelligence (Cambridge, UK). She is in the executive committee of the IEEE global initiative on ethical considerations on the development of autonomous and intelligent systems and she is a member of the board of directors of the Partnership on AI, where she represents IBM as one of the founding partners.
She has been a member of the European Commission High Level Expert Group on AI and the general chair of the AAAI 2020 conference. She co-leads the internal IBM AI Ethics board.
She will be the AAAI president in 2022-2024.
Karen Sparck Jones Lecture
Learning with Limited Labeled Data: The Role of User Interactions
Ahmed H. Awadallah | Microsoft Research
Abstract:
Modern machine learning applications have enjoyed a great boost utilizing neural networks models, allowing them to achieve state-of-the-art results on a wide range of tasks. Such models, however, require large amounts of annotated data for training. In many real-world scenarios, such data is of limited availability due to the time and expense of labelling data and the private and personal nature of some of these datasets. Meanwhile, many real-world applications have rich meta-data in the form of user interaction and feedback. Research on information access and retrieval has demonstrated the benefits of learning from user interaction, for many applications such as ranking search results and enabling users to interactively use the system, refining, and repairing mistakes to reach their goals. In this presentation, I will reflect on lessons learned from work in this area, present some technical solutions and discuss open problems and outstanding challenges.
Short Bio
Ahmed Awadallah is a Principal Research Manager at Microsoft Research where he leads the Language & Information Technologies group. His research interests lie at the intersection of machine learning, natural language processing and information retrieval. Most recently, his research has focused on formulating new learning and evaluation strategies that leverage human interaction and user behavior and building NLP systems in domains where hand-labeled training sets are limited. Ahmed’s work adopts a multi-disciplinary perspective spanning NLP and IR and considerably cares about translating research gains into real-world settings to achieve real-world impact.
Untraditional Medicine
Ophir Frieder | Georgetown University – USA
Abstract
Computing continues to change the landscape of nearly all domains, medicine included. Drug resistance is predicted and avoided via data mining applications and disease outbreak is detected early via text mining techniques. These are but just some examples where computing is reshaping medical practice. Specifically, we describe the monitoring of social media to detect disease outbreak and describe the implications of such surveillance schemes to healthcare planning for a major children-focused hospital. We continue by highlighting a deployed data unification system that integrates dispersed medical history, forming patient-centered, life-long medical records; this integration enables accurate mining approaches, likewise described, that significantly improve drug prescription accuracy. Other medically oriented mining and search applications are mentioned.
Short Bio
Ophir Frieder focuses on scalable information processing systems with particular emphasis on health informatics. He is a Fellow of the AAAS, ACM, AIMBE, IEEE, and NAI, an inaugural Member of the ACM SIGIR Academy, and a Member of Academia Europaea and the European Academy of Sciences and Arts. Heavily involved with industrial efforts, he is the Chief Scientific Officer of Invaryant, Inc. and the Lead Science and Technology Advisor for Aurora: The Business Forge. He is a member of the Information Retrieval Lab and computer science faculty at Georgetown University and the biostatistics, bioinformatics and biomathematics faculty in the Georgetown University Medical Center.