Shalaleh Rismani

Responsible AI measures dataset for ethics evaluation of AI systems

Renee Shelby

Meaningful governance of any system requires the system to be assessed and monitored effectively. In the domain of Artificial Intelligence (… (voir plus)AI), global efforts have established a set of ethical principles, including fairness, transparency, and privacy upon which AI governance expectations are being built. The computing research community has proposed numerous means of measuring an AI system's normative qualities along these principles. Current reporting of these measures is principle-specific, limited in scope, or otherwise dispersed across publication platforms, hindering the domain's ability to critique its practices. To address this, we introduce the Responsible AI Measures Dataset, consolidating 12,067 data points across 791 evaluation measures covering 11 ethical principles. It is extracted from a corpus of computing literature (n = 257) published between 2011 and 2023. The dataset includes detailed descriptions of each measure, AI system characteristics, and publication metadata. An accompanying, interactive visualization tool supports usability and interpretation of the dataset. The Responsible AI Measures Dataset enables practitioners to explore existing assessment approaches and critically analyze how the computing domain measures normative concepts.

2025-12-19

Scientific Data (publié)

International AI Safety Report Second Key Update: Technical Safeguards and Risk Management

Yoshua Bengio

Stephen Clare

Carina Prunkl

Maksym Andriushchenko

BEN BUCKNALL

Philip Fox

Nestor Maslej

Conor McGlynn

Malcolm Murray

Stephen Casper

Jessica Newman

Daniel Privitera

Sören Mindermann

Daron Acemoglu

Thomas G. Dietterich

Fredrik Heintz

Geoffrey Hinton

Nick Jennings

Susan Leavy … (voir 17 de plus)

Teresa Ludermir

Vidushi Marda

Helen Margetts

John McDermid

Jane Munga

Arvind Narayanan

Alondra Nelson

Clara Neppel

Sarvapali D. (Gopal) Ramchurn

Stuart Russell

Marietje Schaake

Bernhard Schölkopf

Alvaro Soto

Lee Tiedrich

Gael Varoquaux

Andrew Yao

Ya-Qin Zhang

This is the Second Key Update to the 2025 International AI Safety Report. The First Key Update (1) discussed developments in the capabilitie… (voir plus)s of general-purpose AI models and systems and associated risks. This Key Update covers how various actors, including researchers, companies, and governments, are approaching risk management and technical mitigations for AI. The past year has seen important developments in AI risk management, including better techniques for training safer models and monitoring their outputs. While this represents tangible progress, significant gaps remain. It is often uncertain how effective current measures are at preventing harms, and effectiveness varies across time and applications. There are many opportunities to further strengthen existing safeguard techniques and to develop new ones. This Key Update provides a concise overview of critical developments in risk management practices and technical risk mitigation since the publication of the 2025 AI Safety Report in January. It highlights where progress is being made and where gaps remain. Above all, it aims to support policymakers, researchers, and the public in navigating a rapidly changing environment, helping them to make informed and timely decisions about the governance of general-purpose AI. Professor Yoshua BengioUniversité de Montréal / LawZero /Mila – Quebec AI Institute & Chair

2025-12-06

SuperIntelligence - Robotics - Safety & Alignment (publié)

International AI Safety Report: First Key Update, Capabilities and Risk Implications

Prof. Yoshua Bengio

Stephen Clare

Carina Prunkl

Maksym Andriushchenko

BEN BUCKNALL

Philip Fox

Tiancheng Hu

Cameron Jones

Sam Manning

Nestor Maslej

Vasilios Mavroudis

Conor McGlynn

Malcolm Murray

Charlotte Stix

Lucia Velasco

Nicole Wheeler

Daniel Privitera

Sören Mindermann

Daron Acemoglu … (voir 36 de plus)

Thomas G. Dietterich

Fredrik Heintz

Geoffrey Hinton

Nick Jennings

Susan Leavy

Teresa Ludermir

Vidushi Marda

Helen Margetts

John McDermid

Jane Munga

Arvind Narayanan

Alondra Nelson

Clara Neppel

Sarvapali D. (Gopal) Ramchurn

Stuart Russell

Marietje Schaake

Bernhard Schölkopf

Alvaro Soto

Lee Tiedrich

Gael Varoquaux

Andrew Yao

Ya-Qin Zhang

Lambrini Das

Claire Dennis

Arianna Dini

Freya Hempleman

Samuel Kenny

Patrick King

Hannah Merchant

Jamie-Day Rawal

Rose Woolhouse

The field of AI is moving too quickly for a single yearly publication to keep pace. Significant changes can occur on a timescale of months, … (voir plus)sometimes weeks. This is why we are releasing Key Updates: shorter, focused reports that highlight the most important developments between full editions of the International AI Safety Report. With these updates, we aim to provide policymakers, researchers, and the public with up-to-date information to support wise decisions about AI governance. This first Key Update focuses on areas where especially significant changes have occurred since January 2025: advances in general-purpose AI systems' capabilities, and the implications for several critical risks. New training techniques have enabled AI systems to reason step-by-step and operate autonomously for longer periods, allowing them to tackle more kinds of work. However, these same advances create new challenges across biological risks, cyber security, and oversight of AI systems themselves. The International AI Safety Report is intended to help readers assess, anticipate, and manage risks from general-purpose AI systems. These Key Updates ensure that critical developments receive timely attention as the field rapidly evolves.

2025-10-21

SuperIntelligence - Robotics - Safety & Alignment (publié)

Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms

Renee Shelby

Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by … (voir plus)high-level ethics principles. These measures are developed and used in fragmented ways, without adequate attention to how they are situated in AI systems. In this paper, we examine how existing measures used in the computing literature map to AI system components, attributes, hazards, and harms. Our analysis draws on a scoping review resulting in nearly 800 measures corresponding to 11 AI ethics principles. We find that most measures focus on four principles – fairness, transparency, privacy, and trust – and primarily assess model or output system components. Few measures account for interactions across system elements, and only a narrow set of hazards is typically considered for each harm type. Many measures are disconnected from where harm is experienced and lack guidance for setting meaningful thresholds. These patterns reveal how current evaluation practices remain fragmented, measuring in pieces rather than capturing how harms emerge across systems. Framing measures with respect to system attributes, hazards, and harms can strengthen regulatory oversight, support actionable practices in industry, and ground future research in systems-level understanding.

2025-10-14

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (publié)

Roboethics for Everyone – A Hands-On Teaching Module for K-12 and Beyond

In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. … (voir plus)Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.

2024-12-19

Proceedings of the Canadian Engineering Education Association (CEEA) (publié)

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems

Roel Dobbe

2024-10-28

ArXiv (prépublication)

How different mental models of AI-based writing assistants impact writers’ interactions with them

Su Lin Blodgett

A.R. Olteanu

Q. Vera Liao

2024-10-14

Proceedings of the Third Workshop on Intelligent and Interactive Writing Assistants (publié)

Driving into the Loop: Mapping Automation Bias and Liability Issues for Advanced Driver Assistance Systems

Katie Szilagyi

Jason Millar

2023-10-06

Digital Society (publié)

Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development

Renee Shelby

Andrew J Smart

Renelito Delos Santos

Negar Rostamzadeh

Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this … (voir plus)work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks – both of which are not designed explicitly examine social and ethical risks – can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model.

2023-08-28

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)

Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

Renee Shelby

Kathryn Henne

Negar Rostamzadeh

Paul Nicholas

N'Mah Yilla-Akbari

Jess Gallegos

Andrew J Smart

Emilio Garcia

Gurleen Virk

2023-08-28

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)

What does it mean to be a responsible AI practitioner: An ontology of roles and skills

With the growing need to regulate AI systems across a wide variety of application domains, a new set of occupations has emerged in the indus… (voir plus)try. The so-called responsible Artificial Intelligence (AI) practitioners or AI ethicists are generally tasked with interpreting and operationalizing best practices for ethical and safe design of AI systems. Due to the nascent nature of these roles, however, it is unclear to future employers and aspiring AI ethicists what specific function these roles serve and what skills are necessary to serve the functions. Without clarity on these, we cannot train future AI ethicists with meaningful learning objectives. In this work, we examine what responsible AI practitioners do in the industry and what skills they employ on the job. We propose an ontology of existing roles alongside skills and competencies that serve each role. We created this ontology by examining the job postings for such roles over a two-year period (2020-2022) and conducting expert interviews with fourteen individuals who currently hold such a role in the industry. Our ontology contributes to business leaders looking to build responsible AI teams and provides educators with a set of competencies that an AI ethics curriculum can prioritize.

2023-08-28

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)