AI Matters: our blog
We are happy to announce that the call for the ACM SIGAI 2022 Industry Award is now available at the main Industry Award page here: https://sigai.acm.org/main/acm-sigai-industry-award/
The ACM SIGAI Industry Award for Excellence in Artificial Intelligence (AI) will be given annually to individuals or teams who have transferred original academic research into AI applications in recent years in ways that demonstrate the power of AI techniques via a combination of the following features: originality of the research novelty and technical excellence of the approach; importance of AI techniques to the approach; and actual or predicted societal impact of the application. Awardees receive a plaque accompanied by a prize of $5,000, and will be recognized at the International Joint Conference on Artificial Intelligence through an agreement with the IJCAI Board of Trustees.
For more information please see the call here: https://sigai.acm.org/main/acm-sigai-industry-award/
The selection committee for the ACM/SIGAI Autonomous Agents Research Award is pleased to announce that Professor Maria Gini is the recipient of the 2022 award. Professor Maria Gini is Professor of Computer Science and Engineering at the University of Minnesota.
For details click here.
ACM SIGAI Webinar: Enlichenment: Insights Towards AI Impact in Education through a Mycelial Partnership between Research, Policy, and Practice
Title: ACM SIGAI Webinar: Enlichenment: Insights Towards AI Impact in Education through a Mycelial Partnership between Research, Policy, and Practice
For Event Registration Please see the ACM Webinar Site: https://webinars.on24.com/acm/rose
Date: Thursday, June 24, 2021
Time: 12:00 PM Eastern Daylight Time
Duration: 1 hour
Summary: As we begin to emerge from COVID-19, in the face of tremendous learning loss and widening achievement gaps, we, as a society, are grappling with envisioning the future of education. In the field of Artificial Intelligence, we ask what our role might be in this emerging reality. This ACM SIGAI Learning Webinar will engage the audience in consideration of these issues in light of insights gained from recent research. Since the early 70s, the field of Artificial Intelligence and the fields of Human Learning and Teaching have partnered together to study how to use technology to understand and support human learning. Nevertheless, despite tremendous growth in these fields over the decades, and notable large-scale success, the emergency move to universal online learning at all levels over the past year has exposed gaps and breakdowns in the path from basic research into practice.
As the new administration reacts by committing to invest substantial research dollars into addressing the “COVID Melt,” or learning loss, we must ask ourselves how to prepare for potentially future emergencies so that such tremendous and inequitable learning loss can be prevented from happening again. The International Alliance to Advance Learning in a Digital Era (IAALDE) is partnering with the American Academy for the Advancement of Science (AAAS) to foster productive synergy between the worlds of research, policy, and practice, beginning with a recent kickoff event. Administrators and policy makers/implementors of policy were invited to engage with world class leading researchers across a broad spectrum of research in technology enhanced learning to accelerate the path from research into real educational impact through practice. The goal is that the work going forward would benefit tremendously from increased grounding from the lived experiences of administrators and implementors of policy in schools. At the same time, that greater awareness of research findings might offer opportunities to reflect and reconsider practices on the ground in schools. This discussion, involving over 100 delegates, was meant to lay the foundation for documents, resources, and activities to move the conversation forward. Find out more about insights learned, next steps, and how you can get involved on June 3!
Speaker: Carolyn P. Rose, Professor, Language Technologies and Human-Computer Interaction, Carnegie Mellon University
Carolyn Rose is a Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. Her research program focuses on computational modeling of discourse to enable scientific understanding of the social and pragmatic nature of conversational interaction of all forms, and using this understanding to build intelligent computational systems for improving collaborative interactions. Her research group’s highly interdisciplinary work, published in over 270 peer reviewed publications, is represented in the top venues of 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards in 3 of these fields. She is a Past President and Inaugural Fellow of the International Society of the Learning Sciences, Senior Member of IEEE, Founding Chair of the International Alliance to Advance Learning in the Digital Era, and Co-Editor-in-Chief of the International Journal of Computer-Supported Collaborative Learning. She also serves as a 2020-2021 AAAS Fellow under the Leshner Institute for Public Engagement with Science, with a focus on public engagement with Artificial Intelligence.
Moderator: Todd W. Neller Professor, Computer Science, Gettysburg College
Todd W. Neller is a Professor of Computer Science at Gettysburg College, and was the recipient of the 2018 AAAI/EAAI Outstanding Educator Award. A Cornell University Merrill Presidential Scholar, he received a B.S. in Computer Science with distinction in 1993. In 2000, he received his Ph.D. with distinction in teaching at Stanford University, where he was awarded a Stanford University Lieberman Fellowship, and the George E. Forsythe Memorial Award for excellence in teaching. His dissertation concerned extensions of artificial intelligence (AI) search algorithms to hybrid dynamical systems, and the refutation of hybrid system properties through simulation and information-based optimization. A game enthusiast, Neller has enjoyed pursuing game AI challenges, computing optimal play for jeopardy dice games such as Pass the Pigs and bluffing dice games such as Dudo, creating new reasoning algorithms for Clue/Cluedo, analyzing optimal Risk attack and defense policies, and designing games and puzzles.
Brookings Webinar: Should the Government Play a Role in Reducing Algorithmic Bias?
On March 12, the Center for Technology Innovation at Brookings hosted a webinar on the role of government in identifying and reducing algorithmic biases (see video). Speakers discussed what is needed to prioritize fairness in machine-learning models and how to weed out artificial intelligence models that perpetuate discrimination. Questions included
How do the European Union, U.K., and U.S. differ in their approaches to bias and discrimination?
What lessons can they learn from each other?
Should approaches to AI bias be universally applied to ensure civil and human rights for protected groups?
They observe that “policymakers and researchers throughout the world are considering strategies for reducing biased decisions made by machine-learning algorithms. To date, the U.K. has been the most forward in outlining a role for government in identifying and mitigating biases and their unintended consequences, especially decisions that impact marginalized populations. In the U.S., legislators and policymakers have focused on algorithmic accountability and the explanation of models to ensure fairness in predictive decision making.”
The moderator was Alex Engler, Rubenstein Fellow – Governance Studies.
Speakers and discussants were
Lara Macdonald and Ghazi Ahamat, Senior Policy Advisors – UK Centre for Data Ethics and Innovation;
Nicol Turner Lee, Brookings Senior Fellow – Governance Studies and Director, Center for Technology Innovation; and
Adrian Weller, Programme Director for AI at the Alan Turing Institute
Algo2021 Conference to Be Held on April 29, 2021
The University College London (Online) will present The Algo2021 Conference: Ecosystems of Excellence & Trust, building upon the success of their 2020 inaugural conference. They will platform all major stakeholders – academia, civil service, and industry – by showcasing the cutting-edge developments, contemporary debates, and perspectives of major players. The 2021 conference theme reflects the desire to promote public good innovation. Sessions and topics include the following:
Machine Learning in Healthcare,
Trust and the Human-on-the-Loop,
Artificial Intelligence and Predictive Policing,
AI and Innovation in Healthcare Technologies,
AI in Learning and Education Technologies,
Building Communities of Excellence in AI, and
Human-AI and Ethics Issues.
Politico’s AI Online Summit on May 31, 2021
The 2021 Summit plans to dissect Europe’s AI legislative package, along with the impact of geopolitical tensions and tech regulations, on topics such as data and privacy concerns. The summit will convene top EU and national decision makers, opinion formers, and tech industry leaders.
“The European Commission will soon introduce legislation to govern the use of AI, acting on its aim to draw up rules for the technology sector over the next five years and on its legacy as the world’s leading regulator of digital privacy. At the heart of the issue is the will to balance the need for rules with the desire to boost innovation, allowing the old continent to assert its digital sovereignty. On where the needle should be, opinions are divided – and the publication of the Commission’s draft proposal will not be the end of the discussion.”
Issues to be addressed are the following:
How rules may fit broader plans to build European tech platforms that compete globally with other regions;
How new requirements on algorithmic transparency might be viewed by regular people; and
What kind of implementation efforts will be required for startups, mid-size companies and big tech.
The Politico 4th edition of the AI Summit will address important questions in panel discussions, exclusive interviews, and interactive roundtable discussions. Top regulators, tech leaders, startups, and civil society stakeholders will examine the EU’s legislative framework on AI and data flow while tackling uncomfortable questions about people’s fundamental rights, misinformation, and international cooperation that will determine the future of AI in Europe and worldwide.
HCAI for Policymakers
“Human-Centered AI” by Ben Shneiderman was recently published in Issues in Science and Technology 37, no. 2 (Winter 2021): 56–61. A timely observation is that Artificial Intelligence is clearly expanding to include human-centered issues from ethics, explainability, and trust to applications such as user interfaces for self-driving cars. The importance of the HCAI fresh approach, which can enable more widespread use of AI in safe ways that promote human control, is acknowledged by the article’s appearance in NAS Issues in Science and Technology. An implication of the article is that computer scientists should build devices to enhance and empower—not replace—humans.
HCAI as described by Prof. Shneiderman represents a radically different approach to systems design by imagining a different role for machines. Envisioning AI systems as comprising machines and people working together is a much different starting point than the assumption and goal of autonomous AI. In fact, a design process with this kind of forethought might even lead to a product not being developed, thus preventing future harm. One of the many interesting points in the NAS Issues article is the observation about the philosophical clash between two approaches to gaining knowledge about the world—Aristotle’s rationalism and Leonardo da Vinci’s empiricism—and the connection with the current perspective of AI developers: “The rationalist viewpoint, however, is dominant in the AI community. It leads researchers and developers to emphasize data-driven solutions based on algorithms.” Data science unfortunately often focuses on the rationalist approach without including the contributions from, and protection of, the human experience.
From the NAS article, HCAI is aligned with “the rise of the concept of design thinking, an approach to innovation that begins with empathy for users and pushes forward with humility about the limits of machines and people. Empathy enables designers to be sensitive to the confusion and frustration that users might have and the dangers to people when AI systems fail. Humility leads designers to recognize the inevitability of failure and inspires them to be always on the lookout for what wrongs are preventable.”
Policymakers need to “understand HCAI’s promise not only for our machines but for our lives. A good starting place is an appreciation of the two competing philosophies that have shaped the development of AI, and what those imply for the design of new technologies … comprehending these competing imperatives can provide a foundation for navigating the vast thicket of ethical dilemmas now arising in the machine-learning space.” An HCAI approach can incorporate creativity and innovation into AI systems by understanding and incorporating human insights about complexity into the design of AI systems and using machines to prepare data for taking advantage of human insight and experience. For many more details and enjoyable reading, go to https://issues.org/human-centered-ai/.
NSCAI Final Report
The National Security Commission on Artificial Intelligence (NSCAI) issued a final report. This bipartisan commission of 15 technologists, national security professionals, business executives, and academic leaders delivered an “uncomfortable message: America is not prepared to defend or compete in the AI era.” They discuss a “reality that demands comprehensive, whole-of-nation action.” The final report presents a strategy to “defend against AI threats, responsibly employ AI for national security, and win the broader technology competition for the sake of our prosperity, security, and welfare.”
The mandate of the National Security Commission on Artificial Intelligence (NSCAI) is to make recommendations to the President and Congress to “advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States.” The 16 chapters in the Main Report contain many conclusions and recommendations, including a “Blueprints for Action” with detailed steps for implementing the recommendations.