Abraham Bernstein - University of Zürich

Keynote: Society Rules – How should Semantic Web be disciplined?

Societies always had structures to gather, retain, maintain, and process knowledge: fables, stories, ceremonies, books, drawings, paintings, architecture … indeed, many if not most cultural products in some way assist knowledge evolution. Research on the Semantic Web was founded on the vision of building an environment where "machines can comprehend documents and data" and "to assist the evolution of knowledge" (Bernes-Lee et al., 2001). Hence, at its core, it pursues the goal of building societal knowledge structures with the help of machines.

As we evolved the technology over the past twenty years, we have made incredible advances. Data is almost available in abundance – sometimes even marked up. Some agents with extraordinary capabilities – not necessarily the ones we had imagined all those years ago – live in our pockets. As our research results are increasingly used, they are both informed and infused by societal rules and norms as well as shape society by providing meaning and perpetuating norms. But what does it mean for us as scientists contributing to the endeavor of "assisting the evolution of knowledge" and our discipline?

Using examples from both the Semantic Web and Recommender Systems, we will explore the implications of this coming of age of the Semantic Web – both as a set of technologies and discipline. Specifically, in a Math or Engineering tradition, our community was largely driven by the goal of developing solutions for problems and understanding their underlying governing principles. Given that we investigate means to assist the societal process of knowledge evolution and its societal rules, the talk will highlight how we should increasingly use the descriptive methods of behavioral sciences. Pushing this thought even further, given that our research results perpetuate societal structures, it will illustrate how we need to increasingly address some of the normative challenges that they contain. Hence, these steps will require us to master many scientific traditions and embrace cultural differences in our research, producing a new understanding of our discipline.

Bio - Abraham Bernstein, Ph.D., is a Full Professor of Informatics at the University of Zurich (UZH), Switzerland. He received a Diploma in Computer Science from ETH Zurich and a Ph.D. in Management with a concentration in Information Technologies from the Sloan School of Management at MIT. His research focuses on various aspects of the semantic web, recommender systems, AI/data mining/machine learning, crowd computing, and collective intelligence. His work is based on both social science (organizational psychology/sociology/economics) and technical (computer science, artificial intelligence) foundations. He is also a founding Director of the University of Zurich's Digital Society Initiative (DSI) — an interdisciplinary research and teaching initiative with more than 180 faculty members ranging from divinity to veterinary medicine investigating all aspects of the interplay between society and the digitalization – and President of the Steering Committee of the Swiss National Science Foundation's Research Priority Program 77 on the Digital Transformation. He was also a member of the Council of Europe's Committee of Experts on human rights dimensions of automated data processing and different forms of artificial intelligence (MSI-AUT). Abraham Bernstein has served on the editorial boards of various top journals, including as a co-Editor in Chief at the Journal of Web Semantics or Associate Editor at the ACM Transactions on Internet Technologies and ACM Transactions on Interactive Intelligent Systems, as well as several advisory boards in both academe and practice. He was elected as an individual member of the Swiss Academy of Technical Sciences in 2019.

Yoelle Maarek - Amazon

Keynote: Alexa, do you have a sense of humor?

In his 1950 seminal paper on “Computing Machinery and Intelligence," Alan Turing discusses things that a computer would arguably never do and lists “be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, etc.” Computational humor is a significant AI challenge; yet, it has received surprisingly little attention from the community. In this session, we explain why it should not be neglected and discuss how we discovered that Amazon customers generate funny comments and issue playful requests to Alexa, Amazon voice AI. We then show that having Alexa automatically recognize and acknowledge when users are joking improves their experience. This work is just the first step towards giving AI assistants a sense of humor, which we believe is critical to build trust in machines. We hope it will spur more research in this fascinating (and fun:) area.

Bio - Yoelle Maarek is a Vice President at Amazon, heading research for Alexa Shopping. Prior to this, she was Vice President of Research at Yahoo, guiding the research teams worldwide. Prior to Yahoo, she was the first Google engineer in Israel and opened the Haifa engineering office. One of the most notable features her team launched is Google Suggest, the query auto-completion service deployed on Google search and Youtube worldwide. Before Google, she was with IBM Research, first in the US, then in Israel, holding a number of positions from Research Staff Member to Distinguished Engineer. She has been serving in various senior roles at leading academic research conferences in search and data mining, such as SIGIR, WWW and WSDM. She is a member of the Technion Board of Governors, was inducted as an ACM Fellow in 2013 and elected to the National Academy of Engineering in 2021. Yoelle obtained a PhD in Computer Science from the Technion, Israel in 1989, holds an engineering degree from the Ecole des Ponts et Chaussées, and a DEA (graduate degree) in Computer Science from Paris VI university, both awarded in 1985.

Julia Stoyanovich - New York University

Keynote: Building Data Equity Systems

Equity as a social concept — treating people differently depending on their endowments and needs to provide equality of outcome rather than equality of treatment — lends a unifying vision for ongoing work to operationalize ethical and legal considerations in socio-technical systems. In my talk I will present a vision for designing, developing, deploying, and overseeing data-intensive systems that consider equity as an essential requirement. I will discuss ongoing technical work in scope of the "Data, Responsibly" project, and will place this work into the broader context of policy, education, and public outreach activities.

Bio. Julia Stoyanovich is an Institute Associate Professor of Computer Science and Engineering at the Tandon School of Engineering, and an Associate Professor of Data Science at the Center for Data Science at New York University (NYU). She directs the Center for Responsible AI at NYU, a hub for interdisciplinary research, public education, and advocacy that aims to make responsible AI synonymous with AI. Julia's research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. She established the Data, Responsibly consortium and served on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio. Julia developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic. In addition to data ethics, Julia works on the management and analysis of preference data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst. Julia is a recipient of an NSF CAREER award and of an NSF/CRA CI Fellowship.