Monthly Archives: January 2023

Posted by Posted by Alvin Rajkomar, Research Scientist, and Eric Loreaux, Software Engineer, Google Research

Today many people have digital access to their medical records, including their doctor’s clinical notes. However, clinical notes are hard to understand because of the specialized language that clinicians use, which contains unfamiliar shorthand and abbreviations. In fact, there are thousands of such abbreviations, many of which are specific to certain medical specialities and locales or can mean multiple things in different contexts. For example, a doctor might write in their clinical notes, “pt referred to pt for lbp“, which is meant to convey the statement: “Patient referred to physical therapy for low back pain.” Coming up with this translation is tough for laypeople and computers because some abbreviations are uncommon in everyday language (e.g., “lbp” means “low back pain”), and even familiar abbreviations, such as “pt” for “patient”, can have alternate meanings, such as “physical therapy.” To disambiguate between multiple meanings, the surrounding context must be considered. It’s no easy task to decipher all the meanings, and prior research suggests that expanding the shorthand and abbreviations can help patients better understand their health, diagnoses, and treatments.

In “Deciphering clinical abbreviations with a privacy protecting machine learning system”, published in Nature Communications, we report our findings on a general method that deciphers clinical abbreviations in a way that is both state-of-the-art and is on-par with board certified physicians in this task. We built the model using only public data on the web that wasn’t associated with any patient (i.e., no potentially sensitive data) and evaluated performance on real, de-identified notes from inpatient and outpatient clinicians from different health systems. To enable the model to generalize from web-data to notes, we created a way to algorithmically re-write large amounts of internet text to look as if it were written by a doctor (called web-scale reverse substitution), and we developed a novel inference method, (called elicitive inference).

The model input is a string that may or may not contain medical abbreviations. We trained a model to output a corresponding string in which all abbreviations are simultaneously detected and expanded. If the input string does not contain an abbreviation, the model will output the original string. By Rajkomar et al used under CC BY 4.0/ Cropped from original.

Rewriting Text to Include Medical Abbreviations

Building a system to translate doctors’ notes would usually start with a large, representative dataset of clinical text where all abbreviations are labeled with their meanings. But no such dataset for general use by researchers exists. We therefore sought to develop an automated way to create such a dataset but without the use of any actual patient notes, which might include sensitive data. We also wanted to ensure that models trained on this data would still work well on real clinical notes from multiple hospital sites and types of care, such as both outpatient and inpatient.

To do this, we referenced a dictionary of thousands of clinical abbreviations and their expansions, and found sentences on the web that contained uses of the expansions from this dictionary. We then “rewrote” those sentences by abbreviating each expansion, resulting in web data that looked like it was written by a doctor. For instance, if a website contained the phrase “patients with atrial fibrillation can have chest pain,” we would rewrite this sentence to “pts with af can have cp.” We then used the abbreviated text as input to the model, with the original text serving as the label. This approach provided us with large amounts of data to train our model to perform abbreviation expansion.

The idea of “reverse substituting” the long-forms for their abbreviations was introduced in prior research, but our distributed algorithm allows us to extend the technique to large, web-sized datasets. Our algorithm, called web-scale reverse substitution (WSRS), is designed to ensure that rare terms occur more frequently and common terms are down-sampled across the public web to derive a more balanced dataset. With this data in-hand, we trained a series of large transformer-based language models to expand the web text.

We generate text to train our model on the decoding task by extracting phrases from public web pages that have corresponding medical abbreviations (shaded boxes on the left) and then substituting in the appropriate abbreviations (shaded dots, right). Since some words are found much more frequently than others (“patient” more than “posterior tibialis”, both of which can be abbreviated “pt”), we downsampled common expansions to derive a more balanced dataset across the thousands of abbreviations. By Rajkomar et al used under CC BY 4.0.

Adapting Protein Alignment Algorithms to Unstructured Clinical Text

Evaluation of these models on the particular task of abbreviation expansion is difficult. Because they produce unstructured text as output, we had to figure out which abbreviations in the input correspond to which expansion in the output. To achieve this, we created a modified version of the Needleman Wunsch algorithm, which was originally designed for divergent sequence alignment in molecular biology, to align the model input and output and extract the corresponding abbreviation-expansion pairs. Using this alignment technique, we were able to evaluate the model’s capacity to detect and expand abbreviations accurately. We evaluated Text-to-Text Transfer Transformer (T5) models of various sizes (ranging from 60 million to over 60 billion parameters) and found that larger models performed translation better than smaller models, with the biggest model achieving the best performance.

Creating New Model Inference Techniques to Coax the Model

However, we did find something unexpected. When we evaluated the performance on multiple external test sets from real clinical notes, we found the models would leave some abbreviations unexpanded, and for larger models, the problem of incomplete expansion was even worse. This is mainly due to the fact that while we substitute expansions on the web for their abbreviations, we have no way of handling the abbreviations that are already present. This means that the abbreviations appear in both the original and rewritten text used as respective labels and input, and the model learns not to expand them.

To address this, we developed a new inference-chaining technique in which the model output is fed again as input to coax the model to make further expansions as long as the model is confident in the expansion. In technical terms, our best-performing technique, which we call elicitive inference, involves examining the outputs from a beam search above a certain log-likelihood threshold. Using elicitive inference, we were able to achieve state-of-the-art capability of expanding abbreviations in multiple external test sets.

Real example of the model’s input (left) and output (right).

Comparative Performance

We also sought to understand how patients and doctors currently perform at deciphering clinical notes, and how our model compared. We found that lay people (people without specific medical training) demonstrated less than 30% comprehension of the abbreviations present in the sample medical texts. When we allowed them to use Google Search, their comprehension increased to nearly 75%, still leaving 1 out of 5 abbreviations indecipherable. Unsurprisingly, medical students and trained physicians performed much better at the task with an accuracy of 90%. We found that our largest model was capable of matching or exceeding experts, with an accuracy of 98%.

How does the model perform so well compared to physicians in this task? There are two important factors in the model’s high comparative performance. Part of the discrepancy is that there were some abbreviations that clinicians did not even attempt to expand (such as “cm” for centimeter), which partly lowered the measured performance. This might seem unimportant, but for non-english speakers, these abbreviations may not be familiar, and so it may be helpful to have them written out. In contrast, our model is designed to comprehensively expand abbreviations. In addition, clinicians are familiar with abbreviations they commonly see in their speciality, but other specialists use shorthand that are not understood by those outside their fields. Our model is trained on thousands of abbreviations across multiple specialities and therefore can decipher a breadth of terms.

Towards Improved Health Literacy

We think there are numerous avenues in which large language models (LLMs) can help advance the health literacy of patients by augmenting the information they see and read. Most LLMs are trained on data that does not look like clinical note data, and the unique distribution of this data makes it challenging to deploy these models in an out-of-the-box fashion. We have demonstrated how to overcome this limitation. Our model also serves to “normalize” clinical note data, facilitating additional capabilities of ML to make the text easier for patients of all educational and health-literacy levels to understand.

Acknowledgements

This work was carried out in collaboration with Yuchen Liu, Jonas Kemp, Benny Li, Ming-Jun Chen, Yi Zhang, Afroz Mohiddin, and Juraj Gottweis. We thank Lisa Williams, Yun Liu, Arelene Chung, and Andrew Dai for many useful conversations and discussions about this work.

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Posted by Marian Croak, VP, Google Research, Responsible AI and Human-Centered Technology <!–

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The last year showed tremendous breakthroughs in artificial intelligence (AI), particularly in large language models (LLMs) and text-to-image models. These technological advances require that we are thoughtful and intentional in how they are developed and deployed. In this blogpost, we share ways we have approached Responsible AI across our research in the past year and where we’re headed in 2023. We highlight four primary themes covering foundational and socio-technical research, applied research, and product solutions, as part of our commitment to build AI products in a responsible and ethical manner, in alignment with our AI Principles.

Theme 1: Responsible AI Research Advancements

Machine Learning Research

When machine learning (ML) systems are used in real world contexts, they can fail to behave in expected ways, which reduces their realized benefit. Our research identifies situations in which unexpected behavior may arise, so that we can mitigate undesired outcomes.

Across several types of ML applications, we showed that models are often underspecified, which means they perform well in exactly the situation in which they are trained, but may not be robust or fair in new situations, because the models rely on “spurious correlations” — specific side effects that are not generalizable. This poses a risk to ML system developers, and demands new model evaluation practices.

We surveyed evaluation practices currently used by ML researchers and introduced improved evaluation standards in work addressing common ML pitfalls. We identified and demonstrated techniques to mitigate causal “shortcuts”, which lead to a lack of ML system robustness and dependency on sensitive attributes, such as age or gender.

Shortcut learning: Age impacts correct medical diagnosis.

To better understand the causes of and mitigations for robustness issues, we decided to dig deeper into model design in specific domains. In computer vision, we studied the robustness of new vision transformer models and developed new negative data augmentation techniques to improve their robustness. For natural language tasks, we similarly investigated how different data distributions improve generalization across different groups and how ensembles and pre-trained models can help.

Another key part of our ML work involves developing techniques to build models that are more inclusive. For example, we look to external communities to guide understanding of when and why our evaluations fall short using participatory systems, which explicitly enable joint ownership of predictions and allow people to choose whether to disclose on sensitive topics.

Sociotechnical Research

In our quest to include a diverse range of cultural contexts and voices in AI development and evaluation, we have strengthened community-based research efforts, focusing on particular communities who are less represented or may experience unfair outcomes of AI. We specifically looked at evaluations of unfair gender bias, both in natural language and in contexts such as gender-inclusive health. This work is advancing more accurate evaluations of unfair gender bias so that our technologies evaluate and mitigate harms for people with queer and non-binary identities.

Alongside our fairness advancements, we also reached key milestones in our larger efforts to develop culturally-inclusive AI. We championed the importance of cross-cultural considerations in AI — in particular, cultural differences in user attitudes towards AI and mechanisms for accountability — and built data and techniques that enable culturally-situated evaluations, with a focus on the global south. We also described user experiences of machine translation, in a variety of contexts, and suggested human-centered opportunities for their improvement.

Human-Centered Research

At Google, we focus on advancing human-centered research and design. Recently, our work showed how LLMs can be used to rapidly prototype new AI-based interactions. We also published five new interactive explorable visualizations that introduce key ideas and guidance to the research community, including how to use saliency to detect unintended biases in ML models, and how federated learning can be used to collaboratively train a model with data from multiple users without any raw data leaving their devices.

Our interpretability research explored how we can trace the behavior of language models back to the training data itself, suggested new ways to compare differences in what models pay attention to, how we can explain emergent behavior, and how to identify human-understandable concepts learned by models. We also proposed a new approach for recommender systems that uses natural language explanations to make it easier for people to understand and control their recommendations.

Creativity and AI Research

We initiated conversations with creative teams on the rapidly changing relationship between AI technology and creativity. In the creative writing space, Google’s PAIR and Magenta teams developed a novel prototype for creative writing, and facilitated a writers’ workshop to explore the potential and limits of AI to assist creative writing. The stories from a diverse set of creative writers were published as a collection, along with workshop insights. In the fashion space, we explored the relationship between fashion design and cultural representation, and in the music space, we started examining the risks and opportunities of AI tools for music.

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Theme 2: Responsible AI Research in Products

The ability to see yourself reflected in the world around you is important, yet image-based technologies often lack equitable representation, leaving people of color feeling overlooked and misrepresented. In addition to efforts to improve representation of diverse skin tones across Google products, we introduced a new skin tone scale designed to be more inclusive of the range of skin tones worldwide. Partnering with Harvard professor and sociologist, Dr. Ellis Monk, we released the Monk Skin Tone (MST) Scale, a 10-shade scale that is available for the research community and industry professionals for research and product development. Further, this scale is being incorporated into features on our products, continuing a long line of our work to improve diversity and skin tone representation on Image Search and filters in Google Photos.

The 10 shades of the Monk Skin Tone Scale.

This is one of many examples of how Responsible AI in Research works closely with products across the company to inform research and develop new techniques. In another example, we leveraged our past research on counterfactual data augmentation in natural language to improve SafeSearch, reducing unexpected shocking Search results by 30%, especially on searches related to ethnicity, sexual orientation, and gender. To improve video content moderation, we developed new approaches for helping human raters focus their attention on segments of long videos that are more likely to contain policy violations. And, we’ve continued our research on developing more precise ways of evaluating equal treatment in recommender systems, accounting for the broad diversity of users and use cases.

In the area of large models, we incorporated Responsible AI best practices as part of the development process, creating Model Cards and Data Cards (more details below), Responsible AI benchmarks, and societal impact analysis for models such as GLaM, PaLM, Imagen, and Parti. We also showed that instruction fine-tuning results in many improvements for Responsible AI benchmarks. Because generative models are often trained and evaluated on human-annotated data, we focused on human-centric considerations like rater disagreement and rater diversity. We also presented new capabilities using large models for improving responsibility in other systems. For example, we have explored how language models can generate more complex counterfactuals for counterfactual fairness probing. We will continue to focus on these areas in 2023, also understanding the implications for downstream applications.

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Theme 3: Tooling and Techniques

Responsible Data

Data Documentation:

Extending our earlier work on Model Cards and the Model Card Toolkit, we released Data Cards and the Data Cards Playbook, providing developers with methods and tools to document appropriate uses and essential facts related to a model or dataset. We have also advanced research on best practices for data documentation, such as accounting for a dataset’s origins, annotation processes, intended use cases, ethical considerations, and evolution. We also applied this to healthcare, creating “healthsheets” to underlie the foundation of our international Standing Together collaboration, bringing together patients, health professionals, and policy-makers to develop standards that ensure datasets are diverse and inclusive and to democratize AI.

New Datasets:

Fairness: We released a new dataset to assist in ML fairness and adversarial testing tasks, primarily for generative text datasets. The dataset contains 590 words and phrases that show interactions between adjectives, words, and phrases that have been shown to have stereotypical associations with specific individuals and groups based on their sensitive or protected characteristics.

A partial list of the sensitive characteristics in the dataset denoting their associations with adjectives and stereotypical associations.

Toxicity: We constructed and publicly released a dataset of 10,000 posts to help identify when a comment’s toxicity depends on the comment it’s replying to. This improves the quality of moderation-assistance models and supports the research community working on better ways to remedy online toxicity.

Societal Context Data: We used our experimental societal context repository (SCR) to supply the Perspective team with auxiliary identity and connotation context data for terms relating to categories such as ethnicity, religion, age, gender, or sexual orientation — in multiple languages. This auxiliary societal context data can help augment and balance datasets to significantly reduce unintended biases, and was applied to the widely used Perspective API toxicity models.

Learning Interpretability Tool (LIT)

An important part of developing safer models is having the tools to help debug and understand them. To support this, we released a major update to the Learning Interpretability Tool (LIT), an open-source platform for visualization and understanding of ML models, which now supports images and tabular data. The tool has been widely used in Google to debug models, review model releases, identify fairness issues, and clean up datasets. It also now lets you visualize 10x more data than before, supporting up to 100s of thousands of data points at once.

A screenshot of the Language Interpretability Tool displaying generated sentences on a data table.

Counterfactual Logit Pairing

ML models are sometimes susceptible to flipping their prediction when a sensitive attribute referenced in an input is either removed or replaced. For example, in a toxicity classifier, examples such as “I am a man” and “I am a lesbian” may incorrectly produce different outputs. To enable users in the Open Source community to address unintended bias in their ML models, we launched a new library, Counterfactual Logit Pairing (CLP), which improves a model’s robustness to such perturbations, and can positively influence a model’s stability, fairness, and safety.

Illustration of fairness predictions that can be mitigated using counterfactual logit pairing.

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Theme 4: Demonstrating AI’s Societal Benefit

We believe that AI can be used to explore and address hard, unanswered questions around humanitarian and environmental issues. Our research and engineering efforts span many areas, including accessibility, health, and media representation, with the end goal of promoting inclusion and meaningfully improving people’s lives.

Accessibility

Following many years of research, we launched Project Relate, an Android app that uses a personalized AI-based speech recognition model to enable people with non-standard speech to communicate more easily with others. The app is available to English speakers 18+ in Australia, Canada, Ghana, India, New Zealand, the UK, and the US.

To help catalyze advances in AI to benefit people with disabilities, we also launched the Speech Accessibility Project. This project represents the culmination of a collaborative, multi-year effort between researchers at Google, Amazon, Apple, Meta, Microsoft, and the University of Illinois Urbana-Champaign. Together, this group built a large dataset of impaired speech that is available to developers to empower research and product development for accessibility applications. This work also complements our efforts to assist people with severe motor and speech impairments through improvements to techniques that make use of a user’s eye gaze.

Health

We’re also focused on building technology to better the lives of people affected by chronic health conditions, while addressing systemic inequities, and allowing for transparent data collection. As consumer technologies — such as fitness trackers and mobile phones — become central in data collection for health, we’ve explored use of technology to improve interpretability of clinical risk scores and to better predict disability scores in chronic diseases, leading to earlier treatment and care. And, we advocated for the importance of infrastructure and engineering in this space.

Many health applications use algorithms that are designed to calculate biometrics and benchmarks, and generate recommendations based on variables that include sex at birth, but might not account for users’ current gender identity. To address this issue, we completed a large, international study of trans and non-binary users of consumer technologies and digital health applications to learn how data collection and algorithms used in these technologies can evolve to achieve fairness.

Media

We partnered with the Geena Davis Institute on Gender in Media (GDI) and the Signal Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC) to study 12 years of representation in TV. Based on an analysis of over 440 hours of TV programming, the report highlights findings and brings attention to significant disparities in screen and speaking time for light and dark skinned characters, male and female characters, and younger and older characters. This first-of-its-kind collaboration uses advanced AI models to understand how people-oriented stories are portrayed in media, with the ultimate goal to inspire equitable representation in mainstream media.

MUSE demo Source: Video Collection / Getty Images.

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Plans for 2023 and Beyond

We’re committed to creating research and products that exemplify positive, inclusive, and safe experiences for everyone. This begins by understanding the many aspects of AI risks and safety inherent in the innovative work that we do, and including diverse sets of voices in coming to this understanding.

• Responsible AI Research Advancements: We will strive to understand the implications of the technology that we create, through improved metrics and evaluations, and devise methodology to enable people to use technology to become better world citizens.
• Responsible AI Research in Products: As products leverage new AI capabilities for new user experiences, we will continue to collaborate closely with product teams to understand and measure their societal impacts and to develop new modeling techniques that enable the products to uphold Google’s AI Principles.
• Tools and Techniques: We will develop novel techniques to advance our ability to discover unknown failures, explain model behaviors, and to improve model output through training, responsible generation, and failure mitigation.
• Demonstrating AI’s Social Benefit: We plan to expand our efforts on AI for the Global Goals, bringing together research, technology, and funding to accelerate progress on the Sustainable Development Goals. This commitment will include $25 million to support NGOs and social enterprises. We will further our work on inclusion and equity by forming more collaborations with community-based experts and impacted communities. This includes continuing the Equitable AI Research Roundtables (EARR), focused on the potential impacts and downstream harms of AI with community based experts from the Othering and Belonging Institute at UC Berkeley, PolicyLink, and Emory University School of Law.

Building ML models and products in a responsible and ethical manner is both our core focus and core commitment.

Acknowledgements

This work reflects the efforts from across the Responsible AI and Human-Centered Technology community, from researchers and engineers to product and program managers, all of whom contribute to bringing our work to the AI community.

Google Research, 2022 & Beyond

This was the second blog post in the “Google Research, 2022 & Beyond” series. Other posts in this series are listed in the table below:

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