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Even if you haven’t tried AI tools that are flooding the market, chances are the companies that make your household products are.
Forum brings together three people – a writer, a coder and a policy expert working on ethics guidelines for AI – to help us make sense of this new generation of tools.
Companies including OpenAI and TikTok have signed on to PAI’s set of guidelines designed to help them be more transparent around generative AI.
Sonam Jindal joined PBS to discuss concerns about how the workers powering AI are treated.
2022 has seen eye-catching developments in AI applications. Work is needed to ensure that ethical reflection and responsible publication practices are keeping pace.
Shalin Jyotishi gives an overview of PAI’s work on workplace AI and job quality.
Partnership on AI will bring together its corporate and nonprofit partners to discuss 2023 priorities, including AI and the workforce, bringing underrepresented voices into AI and deepfakes.
The White House Office of Science and Technology Policy recently collected reactions to their AI Bill of Rights. Several of our Partners and our CEO, Rebecca Finlay, were quoted.
In this edition of Braintrust, a group of experts, including PAI’s CEO, look to innovation strategies from across the globe and highlight the parts they thought might work well in the U.S.
AI tools that can turn simple sentences into images have been in development for some time, but have recently become available to the general public.
The FTC began the next step in its process of gathering information that will help inform whether to propose new privacy regulations for how companies can collect and use consumer data.
After the overturning Roe v. Wade, privacy rights advocates warned that apps and AI tech could make it easier for prosecutors to find women who are seeking to end a pregnancy.
From developing more human-centric AI to overcoming fragmented approaches to ethical AI development, here’s what seven experts predict.
This VentureBeat article highlights PAI’s latest white paper on how collecting and using data can accentuate various forms of biases.
A critical dimension is equity in access to both deepfake detection tools and the capacity to use them.
Alexis Conneau’s work has helped build AI systems that can understand dozens of languages with startling accuracy.
A crisis over a suspicious confession video in Myanmar underscores why we need a coordinated response to discern fact from fiction.
A list of incidents that caused, or nearly caused, harm aims to prompt developers to think more carefully about the tech they create.
A white paper from Partnership on AI provides timely advice on tackling the urgent challenge of navigating risks of AI research and responsible publication.
Secure sourcing will better accommodate readers’ right to know, without compromising journalists’ and sources’ rights.
At artificial-intelligence conferences, researchers are increasingly alarmed by what they see.
To ensure success, people behind algorithm auditing startups increasingly suggest stronger industrywide regulation and standards.
In this episode PAI’s CEO, Rebecca Finlay speaks about protecting user data privacy and human rights, following the US Supreme Court ruling of Dobbs v. Jackson Women’s Health Organization.
PAI’s Rebecca Finlay & EY’s Todd Marlin discuss the importance of documentation and diversity in AI, the use of attribute data in targeted advertising, and more.
Jefferey Brown joins Jessica Miller-Merrell to discuss diversity, equity and inclusion, and accessibility when it comes to AI.
In recent years, the focus of AI developers has been to implement technologies that replace basic human labor. Katya Klinova shares why this is the wrong application for AI.
Madhulika Srikumar chats about managing the risks of AI research, how should the AI community think about the consequences of their research, and more.
On this episode Madhulika Shrikumar discusses their recent work Managing Risk and Responsible Publication.
Rosie Campbell discusses a white paper exploring the current debate over publication norms in AI research.
