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How can a computer discern the meaning of a sentence? By “discern its meaning,” I’m referring to grasping the words within the sentence, their context, nuances, and relationships with other words.

In celebration of Women’s History Month, the Salesforce Women’s Network hosted its inaugural Women in AI Day event on March 26th, 2024 in the Salesforce Tower in San Francisco. The event was a gathering of Salesforce women leading the work on ethical and responsible AI at Salesforce.

We have established a set of five guidelines that build upon our Trusted AI Principles to provide more detailed guidance for the responsible development and implementation of GenAI.

We summarize insights from our FAccT 2022 CRAFT workshop that focused on Concepts of Fairness & Transparency, Applied RAI Practices, Organizational Approaches to RAI & Cultural Change, and Public Policy & Regulation.

We brought together 24 practitioners from 17 organizations to talk about a success in their practice they believed others could emulate or a challenge they were facing & wanted to brainstorm with others. We summarize the insights from 2 breakout discussions: Governance/Accountability & Incentives.

August 5, 2021 In June 2021, four of us from the Salesforce AI Ethics and Conversational Design teams collaborated with the Montreal AI Ethics Institute [https://montrealethics.ai/] (MAIEI) to facilitate a workshop on the responsible creation and implementation of chatbots and conversational assistants. Connor Wright [https://www.linkedin.com/

AI can learn the language of biology to create artificial proteins across multiple protein types that are functional and unseen in nature.

An AI model can generate sequences across natural language and proteins with attributes that go beyond the training distribution

Transformer-inspired tips for enhancing the design of neural networks that process graph-structured data

February 2020 Summary from FAccT 2020 CRAFT Session > AI Ethics practitioners in industry look to researchers for insights on how to best identify and mitigate harmful bias in their organization’s AI solutions and create more fair or equitable outcomes. However, it can be a challenge to apply those

AI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye

We introduce Photon, a live demo of natural language interface to databases based on our latest research in neural semantic parsing. 🔗 https://naturalsql.com/

For all the good that AI can bring, responsible tech companies understand they must recognize, prepare for, and mitigate the potential unintended, harmful effects. That’s why Salesforce sees ethics as foundational to AI — and why we’re sharing a closer look at how we infuse an ethical process into

Morpheus exposes the potential allocative harms of popular pretrained NLP models by simulating inflectional variation. We propose adversarial fine-tuning for mitigating the effects of training only on error-free Standard English data.

Word embeddings inherit strong gender bias in data which can be further amplified by downstream models. We propose to purify word embeddings against corpus regularities such as word frequency prior to inferring and removing the gender subspace, which significantly improves the debiasing performance.

Prototypical Contrastive Learning unifies clustering and contrastive self-supervised learning to push the frontiers of unsupervised learning.

We show that deep neural models can describe common sense physics in a valid and sufficient way that is also generalizable. Our ESPRIT framework is trained on a new dataset with physics simulations and descriptions that we collected and have open-sourced.

Summary We investigate NVIDIA's Triton (TensorRT) Inference Server [https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html] as a way of hosting Transformer Language Models. The blog is roughly divided into two parts: (i) instructions for setting up your own inference server, and (ii) benchmarking experiments. The

Learn about the ethical implications of voice for business and how to make them an operational and strategic priority now—before you’re too far down the path.

Earlier this year, Danielle Cass and I ran a workshop with 23 ethical AI practitioners from 15 organizations and shared out insights of what they are doing that has been successful and the open questions or challenges they are working through. A few months later, Matt Marshall, Founder of VentureBeat,

Published: July 10, 2019 As a discipline, those of us working on ethical or responsible AI, are learning together how to translate ethical principles into business practices that work for each of our organizations. The result is a shared community of practice. In this blog post, we share early lessons

Published: May 30, 2019 With the advent of low-code developer tools, people of varying skill levels and backgrounds — not just those with PhDs — are not only able to leverage the benefits of artificial intelligence (AI), but also build intelligent systems. As access to AI widens and the depth of its

This is my obsessively curated list of research papers and articles on ethics in AI that I have been collecting over the years. Ones in bold are those that I refer back to and found particularly useful. Let me know if I am missing your favorites.

If you are thinking about incorporating ethics into your company's culture or product development cycle, check out this list of dozens of ethical tools before you try recreating the wheel.

You may be the first person in your company focusing on the need to create or implement ethical AI. If so, I’d like to share some lessons learned that could help you in your work.

This post is for those responsible for implementing AI systems but do not have a background in data science, statistics, or probability. The intention is to create an approachable introduction to the key concepts to identify potential bias (error) in their training data.

What kind of world do we want for ourselves, those we love, and future society? How do we are organizations, employees, customers, and members of society ensure that technology is in service of society and not the other way around?

In this article, I will focus on mechanisms for removing exclusion from your data and algorithms. Of all the interventions or actions we can take, the advancements here have the highest rate of change.

This is part one of a two-part series about how to build ethics into AI. Part one focuses on cultivating an ethical culture in your company and team, as well as being transparent within your company and externally.