Northeastern University Launches the Institute for Experiential AI

Gil Press from Forbes

Wednesday April 20, 2022

How to do AI right? How to make AI successful in the real world? What is the best way to help traditional companies and industries implement AI? Aiming to answer these questions, Northeastern University launched earlier this month a new research center, the Institute for Experiential AI.

Can We Trust AI – And Is That Even the Right Question?

Oliver Peckham from Datanami

Friday April 8, 2022

AI is becoming more ubiquitous, from everyday voice assistants and online shopping to healthcare and workplace management — but can we trust it? That question was the headline of a panel called “Can We Trust AI?” during the Discover Experiential AI event at Northeastern University’s Institute for Experiential AI (EAI) this week.

The Most Cutting-Edge Technological Innovation in the Future Might Be… Humans

Molly Callahan of [email protected]

Wednesday April 6, 2022

Thought-leaders of all kinds explained at the kickoff event for Northeastern University’s Institute for Experiential Artificial Intelligence that companies ­must integrate information and innovation built upon insight from artificial intelligence, combined with human decision-making, to stay relevant in the future.

How AI Could Propel the Next Generation of Noise-Canceling Headphones

Tanner Stening of [email protected]

Wednesday April 6, 2022

Lila Snyder, chief executive officer of Bose, gives a keynote during the Institute for Experiential Artificial Intelligence event held in East Village 17th floor.

World’s First Chief Data Officer Launches AI Research Center in Boston

Hannah Green of the Boston Business Journal

Monday April 4, 2022

The Boston Business Journal sat down with Usama Fayyad, to discuss why our institute is “fundamentally different than what every other academic institution is doing" by focusing on keeping humans in the decision-making loop.

Toward Trustworthy AI: Bridging the Trust Gap Between Humans and Intelligent Machines

Usama Fayyad

Tuesday March 29, 2022

As artificial intelligence (AI) technology becomes more mainstream in a wide range of applications, settings and domains, we must address the issues of establishing trust in AI systems. Practitioners, users and organizations need to trust how a system reaches decisions, how it operates and the fact that it won't exhibit erratic or dangerous behavior.

Language Models Fail to Say What They Mean or Mean What They Say

Ricardo Baeza-Yates

Tuesday March 29, 2022

Over the past five years, large language models have taken the world by storm. And we’ve seen chatbots become a commodity that anyone can use to provide customer service or other conversational tasks using a simple API. l

Institute for Experiential AI Director of Research Discusses Responsible AI with ODBMS Industry Watch

Roberto V. Zicari

Monday February 7, 2022

Ricardo Baeza-Yates, director of research for the Institute for Experiential AI, met with Roberto V. Zicari from ODBMS Industry Watch to discuss AI ethics, responsible AI, and his take on the regulation of technology.

Advancing AI With Data And Machine Learning: What Else Is Needed?

Usama Fayyad

Friday November 19, 2021

By making AI and ML algorithms work in practice, solving real problems with real constraints and challenges, we advance both the science and education of AI. Our AI Solutions Factory at the Institute for Experiential AI gives learners and apprenticeship-intensive experience and tools that teach the art of practicing AI and Data Science.

10 Key Questions Every Company Should Ask Before Using AI

Ricardo Baeza-Yates

Monday August 23, 2021

If AI technology and machine learning (ML) were parents, deep learning (DL) would be their most successful child, the one who learns from its own mistakes and makes better decisions than the neural networks of generations before. Although deep learning requires big data and most companies only have access to small data, DL is taking the world by storm. But no parent or child is ever perfect, and as the usage of AI grows exponentially, so have the number of AI incidents.

The Attention Economy and the Impact of AI

Ricardo Baeza-Yates & Usama M. Fayyad

Thursday July 1, 2021

The growing ubiquity of the Internet and the information overload created a new economy at the end of the twentieth century: the economy of attention.

Did You Find It on the Internet?

Cansu Canca

Tuesday June 1, 2021

Ethical Complexities of Search Engine Rankings - Search engines play a crucial role in our access to information. We need a more rigorous public debate that goes beyond principles and engages with necessary value trade-offs.

Towards Intellectual Freedom in an AI Ethics Global Community

Ricardo Baeza-Yates et. al.

Tuesday April 13, 2021

The recent incidents involving Dr. Timnit Gebru, Dr. Margaret Mitchell, and Google have triggered an important discussion emblematic of issues arising from the practice of AI Ethics research. We offer this paper and its bibliography as a resource to the global community of AI Ethics Researchers.

AI & Human Values

Baeza-Yates, Bonchi, Crawford, Devillers and Salobir

Sunday February 7, 2021

Inequalities, Biases, Fairness, Nudge, and Feedback Loops — an overview of key concepts and definitions relevant for the study of inequalities and Artificial Intelligence

Northeastern Names Usama Fayyad to Lead Institute of Experiential Artificial Intelligence

Ian Thomsen

Friday October 9, 2020

Usama Fayyad, a longtime leader in data science and AI, will be joining Northeastern as the inaugural executive director for the Institute of Experiential Artificial Intelligence.

Puzzle-solving in AI Ethics: Theories, Principles, and Tools

Cansu Canca

Wednesday May 11, 2022

Cansu Canca, ethics lead at the Institute for Experiential AI (EAI), situates AI ethics and its core questions within relevant disciplines in this Expeditions in Experiential AI Seminar.

Faculty Friday: Improving Deep Learning Algorithms

Xue Shelley Lin

Friday May 6, 2022

Xue Shelley Lin, assistant professor of electrical and computer engineering, discusses how she is investigating and working to improve deep learning techniques to be used in a variety of new applications.

Faculty Friday: Using AI to Help People Live Better and Age Better

Misha Pavel

Friday April 29, 2022

In our newest Faculty Friday video, Professor of the Practice Misha Patel explains how researchers can improve outcomes for those who may be at risk of dementia, Alzheimer’s, or other diseases.

Semantic Scholar, NLP, and the Fight Against COVID-19

Oren Etzioni

Wednesday April 27, 2022

Etzioni to discuss projects focused on advancing AI for the common good, and efforts both inside and outside of the Semantic Scholar project, to garner insights into COVID-19 and its treatment using advanced NLP techniques.

Faculty Friday: Control Theory and Optimization in Engineering Systems

Laurent Lessard

Friday April 22, 2022

Laurent Lessard, associate professor of mechanical and industrial engineering, discusses his work and how it makes a difference in familiar systems we use every day.

Faculty Friday: Discover Experiential AI Virtual Photo Album

Friday April 15, 2022

This week for Faculty Friday, we want to pay special tribute to all of the fantastic faculty members, students, and guest speakers who helped make our Discover Experiential AI launch event a success!

Learning Strong Inference Models in Small Data Domains

Sarah Ostadabbas

Wednesday April 13, 2022

Ostadabbas's research aims to explore a generalized ML approach to solve the small data problem that leverages existing research and fills in key gaps with original work.

Discover Experiential AI: Launching the Institute for Experiential AI

The Institute for Experiential AI

Wednesday April 6, 2022

The Institute for Experiential AI at Northeastern University invites you to join us as we celebrate the launch of our foundational new research center and AI Solutions Factory.

AI Career Fair

The Institute for Experiential AI

Wednesday April 6, 2022

The AI Career Fair is an opportunity for companies and business leaders to connect and network with a talented pool of students who have experience building applied AI solutions.

AI Poster Sessions

The Institute for Experiential AI

Wednesday April 6, 2022

Postdoctoral scholars present cutting-edge research projects that advance the science and enhance the state-of-the-art in AI.

Faculty Friday: Experiential AI Can Help Achieve Business and Research Goals

The Institute for Experiential AI

Friday April 1, 2022

We’re counting down to the Institute for Experiential AI’s launch event with a teaser video featuring our executive director, Usama Fayyad, and other EAI faculty as they discuss how AI impacts their work.

Faculty Friday with Dr. Karen Quigley: Using Machine Learning to Find Unique Physiological Signatures of Human Feelings

Karen Quigley

Friday March 25, 2022

Professor of Psychology, Karen Quigley, shares how she uses machine learning to help identify the physiological signatures that occur as people experience feelings when engaging in real-world settings.

Augmenting Clinical Decision Making with Artificial Intelligence

Jenna Wiens

Wednesday March 23, 2022

Though the potential of artificial intelligence (AI) in healthcare warrants genuine enthusiasm, meaningful impact will require careful integration into clinical care.

Faculty Friday with Lawson Wong: Using Reinforcement Learning to Help Robots Better Understand Human Environments

Lawson Wong

Friday March 18, 2022

Assistant Professor of Computer Science, Lawson Wong, discusses how reinforcement learning helps robots completely understand the complexity of their environments to inform decision-making and work better in human settings.

The Emergent Structure of the Online Information Ecosystem

David Lazer

Wednesday March 9, 2022

This presentation examines the emergent and sometimes paradoxical logic of the Internet news ecosystem and why diversity of news consumption may not result in diversity of content.

Faculty Friday with Sarah Ostadabbas: Enabling Advanced AI Algorithms to Solve Visual Perception Problems in the Small Data Domain

Sarah Ostadabbas

Friday March 4, 2022

Northeastern University Assistant Professor Sarah Ostadabbas explains how her multidisciplinary research in machine learning and pattern recognition can solve visual perception problems in the small data domain.

Artificial Intelligence: An Ethical Overview

MWC Barcelona

Tuesday March 1, 2022

Ricardo Baeza-Yates and Meritxell Borrás will present at the Mobile World Conference in Barcelona, Spain.

Faculty Friday with Chris Amato: Training Intelligent Machines Through Real-World Interactions

Christopher Amato

Friday February 25, 2022

In this week's Faculty Friday video, Northeastern University Assistant Professor and EAI faculty member Chris Amato describes how reinforcement learning enables intelligent interactions between humans and machines.

Ricardo Baeza-Yates presents: Ethics Challenges in AI

15th ACM International WSDM Conference

Thursday February 24, 2022

Director of AI Research Ricardo Baeza-Yates will present at the 15th ACM International WSDM Conference in a session chaired by Jiliang Tang.

Scoring Systems: At the Extreme of Interpretable Machine Learning

Cynthia Rudin

Wednesday February 23, 2022

In this talk, Rudin will focus on one of the most fundamental and important problems in the field of interpretable machine learning: optimal scoring systems.

Faculty Friday with John Basl: Applying Insights from Ethics and Philosophy to Reduce Bias in AI

John Basl

Friday February 18, 2022

Associate Professor John Basl describes how he applies principles from philosophy to advance technical artificial intelligence research with ethics at the core.

Combating performance and accuracy trade-offs in deep learning systems

Alexandros Koliousis

Wednesday February 16, 2022

In this talk, Alexandros will present on the challenging tradeoff between performance and accuracy in deep learning systems, and participants will have the opportunity to join in on the discussion for a Q&A section.

Faculty Friday – Silvio Amir: Using Artificial Intelligence and Machine Learning to Quantify Social Media Data

Silvio Amir

Friday February 11, 2022

Using Artificial Intelligence and Machine Learning to Advance Earth System Science

Auroop Ganguly joins event panel

Monday February 7, 2022

Northeastern and EAI Professor Auroop Ganguly weighs in how AI and ML can help make progress against the challenges arising from Earth science, human-engineered, and social systems.

Faculty Friday – Olga Vitek: Using AI to Understand and Fight Disease

Olga Vitek

Friday February 4, 2022

Northeastern University Professor Olga Vitek explains how artificial intelligence and machine learning can help us better understand disease progression and help discover new intervention methods.

Faculty Friday: Northeastern University Provost David Madigan Shares a Promising Outlook for the Future of AI

David Madigan

Friday January 28, 2022

Northeastern University Provost, David Madigan, discusses how initiatives like the Institute for Experiential AI build a promising future by strengthening ties to partnerships offering real-world applications for AI.

Legends of Data & AI: Just Machine Learning

Usama Fayyad

Friday January 21, 2022

Tina Eliassi-Rad discusses situations where machine learning and data science were key ingredients to success, machine learnings deep data dependence, and the origins of Just ML and what the project is working to do.

Faculty Friday with Sina Fazelpour: The Impact of Group Decision-Making on Responsible AI and Training Data

Sina Fazelpour

Friday January 14, 2022

Sina Fazelpour shares how the decisions made by groups of individuals with their own backgrounds, identities, and views may directly impact AI training data and how those decisions could lead to negative consequences.

Demystifying Artificial Intelligence: The Strategic Role of AI and Data

Usama Fayyad

Thursday January 13, 2022

Join EAI and the The Roux Institute at Northeastern University on Jan. 13 from 1-2pm ET as Dr. Usama Fayyad breaks down what is myth vs. reality in the world of AI and how humans play an integral role in the entire picture.

Faculty Friday with Auroop Ganguly

Auroop Ganguly

Friday December 17, 2021

Using neural networks to harness complex connections between systems

Faculty Friday with Asst Professor Amy Mueller: Innovating methods to collect data for AI-powered environmental sciences

Amy Mueller

Friday December 17, 2021

Check out how Northeastern Asst Professor Amy Mueller is using creative AI methods to gain deeper understandings of the environment that bypass the cost demands of Big Data.

Global Perspectives on AI Ethics

Panelists Cummings, Baeza-Yates, Polisena

Tuesday December 14, 2021

This event is jointly organized by The Governance Lab, NYU Tandon School of Engineering, Global AI Ethics Consortium (GAIEC), Center for Responsible AI @ NYU (R/AI), and the TUM Institute for Ethics in Artificial Intelligence

EAI Core Faculty Member Ajay Satpute Shares How AI is Helping to Advance the Understanding of Emotions

Ajay Satpute

Friday December 10, 2021

Celebrate EAI’s Faculty Friday with Assistant Professor Ajay Satpute. In this insightful Q&A, Ajay shares how he is using AI to understand individual differences in social cognition and emotion.

Building and Auditing Fair Algorithms

Christo Wilson

Wednesday December 8, 2021

In this study, we partnered with Pymetrics, a startup that offers candidate screening services for employers, to conduct an algorithm audit of their machine learning pipeline.

Big Data vs. Right Data

Ricardo Baeza-Yates

Friday December 3, 2021

Hosted by NEOACM - Join this free, virtual presentation and Q&A to hear Ricardo Baeza-Yates address the opportunities and the challenges behind big data.

Faculty Fridays – Featuring David Lazer

David Lazer

Friday December 3, 2021

Introducing EAI Faculty Fridays! Each week we’ll share the work of one of our amazing EAI faculty members.

The Decision-Making Side of Machine Learning

Michael I. Jordan

Wednesday November 17, 2021

Much of the recent focus in machine learning has been on the pattern-recognition side of the field. I will focus instead on the decision-making side, where many fundamental challenges remain.

Learning Interpretable Models on Complex Medical Data

Jennifer G. Dy

Wednesday November 10, 2021

For clinicians and domain scientists to trust and use the results of learning algorithms, not only must models be accurate, but it is also imperative for learning models to be interpretable.

Ethics and Computational Systems

Lorena Jaume-Palasí

Wednesday October 27, 2021

The lecture will illustrate with concrete cases why some approaches to ethics are too narrow to offer the adequate frame to address moral conflicts within technology.

Algorithmic Fairness in Predicting Opioid Use Disorder using Machine Learning

Angela Kilby

Wednesday October 13, 2021

This paper constructs a machine learning algorithm to predict opioid use disorder risk using commercially available claims data.

On the Dangers of Stochastic Parrots

Emily M. Bender

Wednesday September 29, 2021

How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks.

Just Machine Learning

Tina Eliassi-Rad

Wednesday September 15, 2021

Aspirational data are useful for recognizing and detecting sources of unfairness in machine learning models besides biased data - Tina Eliassi-Rad presents on Impossibility Results.

Human-AI Cooperation in eDiscovery

Dave Lewis

Wednesday June 30, 2021

The explosive growth of born-digital documents and messaging, as well as legal changes in the US and elsewhere in the early 2000's, made eDiscovery a test case for human-AI teaming.

Ethics in AI: A Challenging Task

Ricardo Baeza-Yates

Wednesday June 16, 2021

We cover five current specific challenges through examples, and four generic challenges, then discuss what we can do to address these challenges in the near future.

Neural Databases: Natural Language Generation meets Databases

Fabrizio Silvestri

Wednesday June 2, 2021

We asked ourselves a question: can we build a database management system that doesn’t rely on the fundamental concept of a schema?

What We Owe to Decision Subjects

Basl, Behrends, Grant

Wednesday May 19, 2021

In this talk we explore the foundations of the problem of algorithmic opacity and develop an approach for grounding and explaining when opacity is problematic.