Call: Human-Centered AI workshop at NeurIPS 2021

Call for Papers

Human-Centered AI workshop
At NeurIPS 2021 Conference on Neural Information Processing Systems
Monday 13 December 2021
Online
https://sites.google.com/view/hcai-human-centered-ai-neurips/home

Submission deadline (Extended): 25 September 2021 AoE

Human-Centered AI (HCAI) is an emerging discipline that aims to create AI systems that amplify and augment human abilities and preserve human control in order to make AI partnerships more productive, enjoyable, and fair. Our workshop aims to bring together researchers and practitioners from the NeurIPS and HCI communities and others with convergent interests in HCAI. With an emphasis on diversity and discussion, we will explore research questions that stem from the increasingly wide-spread usage of machine learning algorithms across all areas of society, with a specific focus on understanding both technical and design requirements for HCAI systems, as well as how to evaluate the efficacy and effects of HCAI systems.

KEYNOTE SPEAKERS

  • CECILIA ARAGON, University of Washington, US. Dr. Aragon founded and directs the Human Centered Data Science Lab at University of Washington. Her research focuses on enabling humans to gain insights from large datasets through a combination of machine learning and qualitative, quantitative, and visualization analyses. Dr. Aragon’s book, Human Centered Data Science, will be published by MIT Press in 2022.
  • BARBARA POBLETE, University of Chile; Millenium Institute on Data, Chile; Amazon. Dr. Poblete co-directs the “Fake News and Misinformation” multidisciplinary research group at the Millenium Institute on Data. Her research areas are Social Network Analysis, Web Data Mining, Crisis Informatics and Applied Machine Learning. Her work “Information Credibility on Twitter” was awarded the 2021 Seoul Test of Time Award by the IW3C2 at The Web Conference.
  • WENDY MACKAY, Inria; Université Paris-Saclay, France. Dr. Mackay directs the ExSitu research group in HCI at Inria and Université Paris-Saclay. Through study of users who push the limits of interaction and their use patterns regarding complex phenomena, Dr. Mackay explores the future of interactive technologies for creative professionals, with a particular focus on human-AI interaction and collaboration. She is an ACM Fellow and the 2021-22 Computer Science Chair for the Collège de France.
  • CYNTHIA RUDIN, Duke University, US. Dr. Rudin’s research focuses on machine learning tools that help humans make better decisions, mainly interpretable machine learning and interpretable deep learning with domain-based constraints. She applies these methods to critical societal problems in criminology, healthcare, and energy grid reliability, as well as to computer vision.

THEMES

Submissions to the workshop may address one or more of the following themes – or other relevant themes of interest:

  • THEORETICAL FRAMEWORKS, DISCIPLINES AND DISCIPLINARITY. How we approach AI and data science depends on the “lenses” that we bring, based in theory and in practice. Through what perspectives do you approach this complex domain?
  • EXPERIENCES AND CASES WITH AI SYSTEMS. Theories suggest studies and experience reports. Studies and experience reports inform theories. What cases or experiences of human-AI interactions can you contribute to our inter-disciplinary knowledge and discussion?
  • DESIGN FRAMEWORKS FOR HUMAN INITIATIVE AND AI INITIATIVE. Scholars have debated the question of who should have initiative or control between human and AI for over 70 years. What forms of discrete or shared initiative are possible now, and how can we include these possibilities in our systems?
  • EXPERIENCES AND CASES WITH HUMAN-AI COLLABORATION. Design frameworks can inform applications. Experiences with applications can challenge frameworks, or lead to new frameworks. What cases or experiences of human-AI collaborations can you contribute to our inter-disciplinary knowledge and discussion?
  • FAIRNESS AND BIAS. Machine learning-based decision-making systems have the potential to replicate or even exacerbate social inequeties and discrimination. As a result, there is a surge of recent work on developing machine learning algorithms with fairness constraints or guarantees. However, for these tools to have positive real-world impact, their design and implementation should be informed by a clear understanding of human behavior and real needs. What is the interplay between algorithmic fairness and HCI?
  • PRIVACY. In many important machine learning tasks – e.g. those related to healthcare – there is much to be gained from training on personal information, but we must take care to respect individuals’ privacy appropriately. In this workshop, we are particularly interested in understanding specific use cases and considering costs and benefits to individuals and society of making use of private data.
  • TRANSPARENCY, EXPLAINABILITY, INTERPRETABILITY, AND TRUST. We are interested to understand what specific types of explainability or interpretability are helpful to whom in concrete settings, and in exploring any tradeoffs which are inevitably faced.
  • USER RESEARCH. What do we need to know in order to create or enhance an AI-based system? Our engineering heritage suggests that we seek user needs and resolve user pain points. How does our user research for these concepts change with AI systems? Are there other user research goals that are now possible with more sophisticated AI resources and implementations?
  • ACCOUNTABILITY. When people engineer (or create) an AI system and its data, how do we hold them and ourselves accountable for design decisions and outcomes?
  • AUTOMATION OF AI. It is tempting to apply AI to AI, in the form of automated AI. Is this a credible approach? Does human discernment play a role in creating AI systems? Is this a necessary role?
  • EVALUATION. What are the appropriate measurement concepts and resulting metrics to assess our AI systems? How do we balance among efficiency, explainability, understandability, user satisfaction, and user hedonics?
  • GOVERNANCE. Consequential machine learning systems impact the lives of millions of people in areas such as criminal justice, healthcare, education, credit scoring or hiring. Key concepts in the governance of such systems include algorithmic discrimination, transparency, veracity, explainability and the preservation of privacy. What is the role of HCI in relation to the governance of such systems?
  • PROBLEMATIZING DATA. Data initially seem to be simple and “objective.” However, a growing body of evidence shows the often-hidden role of humans in shaping the data in AI. Should we design our systems to strengthen human engagement with data? or to reduce human impact on data?
  • QUALITATIVE DATA IN DATA SCIENCE. Quantitative data analyses may be powerful, but often decontextualized and potentially shallow. Qualitative data analyses may be insightful, but often limited to a narrow sample. How can we combine the strengths of these two approaches?
  • VALUES AND ETHICS OF AI. Values and ethics are necessarily entangled with localized, situated, and culturally-informed human perspectives. What are useful frameworks for a comparative analysis of values and ethics in AI?

HOW TO APPLY

We invite your submission based on the workshop themes (above) or related themes from your own work.

Your submission may take either of the following two forms, using the NeurIPS conference templates (https://neurips.cc/Conferences/2020/PaperInformation/StyleFiles):

  • Short abstracts up to 2 pages
  • Longer papers up to 4 pages

Please send your submissions to michael_muller@us.ibm.com.

IMPORTANT DATES

EXTENDED Due date: 25 September 2021, Anywhere on Earth
Notification date: 15 October 2021

ORGANIZERS

Michael Muller, IBM Research, Cambridge MA USA on unceded lands of Wampanoag and Massachusett Nations
Plamen Agelov, Lancaster University, Lancaster England UK
Shion Guha, University of Toronto, Toronto Ontario Canada
Marina Kogan, University of Utah, Salt Lake City UT USA
Gina Neff, Oxford Internet Institute, Oxford England UK
Nuria Oliver, Data-Pop Alliance and Vodafone Institute, New York NY USA
Manuel Gomez Rodriguez, Max Planck Institute, Kaiserslautern Germany
Adrian Weller, University of Cambridge and Alan Turing Institute, London England UK

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