Friday, December 23rd, 2022

Responsible AI

Dima Fayyad

AI Ethics and Responsible AI aim for AI systems that benefit individuals, societies, and the environment. It encompasses all the ethical, legal, and technical aspects of developing and deploying beneficial AI technologies. It includes making sure your AI system does not cause harm, interfere with human agency, discriminate, or waste resources. 

AI Ethics and Responsible AI is a multidisciplinary research area where an exceptional team of computer scientists, philosophers, legal scholars, sociologists, psychologists, and many other experts work together to make progress and shape the future of technology. 

The institute draws on Northeastern's strengths and existing interdisciplinary collaborations to translate ethics into practice. We work with academic and industry partners to establish guidelines for AI ethics governance mechanisms, translate abstract values into practical guiding principles, and install ethics training for AI practitioners. 

The body of ethics resources we are working to build addresses context-specific challenges related to factors such as fairness, bias, autonomy, diversity, transparency, explainability, and privacy. Drawing on a close working relationship with the Northeastern Ethics Institute, EAI works to produce concrete, action-guiding tools for developing AI applications and research across domains like health, law and criminal justice, finance, and social media.

Latest Research

Natural Language Processing

  GFT (General Fine-Tuning) Recently, deep nets have demonstrated significant progress with exciting results. Much of this work has been reported in leading media outlets and academic conferences such as ACL and NeurIPS. We have developed a “little” language, GFT, that makes deep nets look like regression. GFT is approachable to a broad audience, and […]


At the Institute for Experiential AI, we care about people’s health and wellness. We have several core faculty members with AI health expertise who cover AI for healthcare from multiple scales and various heterogeneous data sources. They work closely with scientists, clinicians, and healthcare providers in designing and developing AI algorithms over a wide range […]

Life Sciences

We live in the Golden Age of Life Sciences, driven in part by transformative advances across multiple fronts in our understanding of biological systems and their applications.  However, we are still only at the beginning of this new chapter.  A systematic strategy that includes humans and AI/ML-based approaches can accelerate the “exponentialization” of this value […]

Climate, Sustainability, and Environment

From climate science and water sustainability to urban resilience and disaster preparedness, Artificial Intelligence has shown significant potential to inform and enable transformative action in the areas of climate, sustainability, and the environment. For example, machine learning, graph analytics, agent-based models, and human-AI systems have shown potential with predictive understanding in earth systems science and […]


AI has a lot of potential for cybersecurity applications, such as predicting attacker behavior, learning from existing cyber incidents, and taking proactive defensive measures to protect critical infrastructures. Historically, we proposed techniques for detecting advanced cyberattacks in enterprise networks based on creating semantic representations of network logs and endpoint data, using a range of supervised […]

Irresponsible AI Atlas

Credit: Irresponsible AI Atlas by John Alexis Guerra Gómez