Emily M. Bender, Yejin Choi named to TIME100 AI list

TIME included two 91探花professors on its first TIME100 AI list, which highlights 100 individuals who are advancing major conversations about how artificial intelligence is reshaping the world.

The list features聽leaders, policymakers, artists and entrepreneurs across a variety of fields and countries. , professor of linguistics, and , professor聽in the Paul G. Allen School of Computer Science & Engineering, were honored as top thinkers.

Bender has consistently raised ethical concerns regarding large language models and has resisted the notion that AI systems are truly intelligent. 鈥渁 machine-learning myth buster,鈥� who is working to dispel 鈥渙verblown promises about what AI can do.鈥�

Among other topics, Bender studies the societal impacts of language technology, the implications for research and design and how to integrate it into the natural language processing curriculum. She was named an AAAS fellow in 2022.

Choi, a MacArthur Fellow, focuses on discerning the various distinctions between human intelligence and AI. She researches whether AI can develop common sense and a sense of humor. Choi is now working to develop AI systems that can comprehend social and moral norms.

鈥淎 calculator can calculate better and faster than I do,鈥� Choi , 鈥渂ut it doesn鈥檛 mean that a calculator is superior to any of us in other dimensions of intelligence.鈥�

The full TIME100 AI list is available on .

President Ana Mari Cauce receives Decrees Award for 鈥榗ontribution to society鈥�

91探花 President Ana Mari Cauce received a , which recognizes people and institutions that add value to and promote the economic and social improvement of Le贸n, Spain.

The awards were given for the first time in 2022. They are granted annually by the Association of Friends of the Decrees, which organizes the public reading of the Decrees of Le贸n of 1188 before the Royal Abbey of San Isidoro de Le贸n.聽These documents contain the oldest known written information about the European parliamentary system.

President Cauce was honored for overseeing the launch of the in 2010. About 1,200 students have participated in 70 programs at the center, and the faculty includes representatives from 20 departments on all three 91探花campuses.

College of Built Environments鈥� Jeff Hou elected to American Society of Landscape Architects鈥� Council of Fellows

, 91探花professor of landscape architecture, has been named a Fellow of the American Society of Landscape Architects.

Election to the ASLA Council of Fellows is based on members鈥� .

鈥淟andscape architects help build a better world for all of us, and ASLA Fellows represent the most respected and accomplished professionals in the entire field,鈥� said ASLA President Emily O’Mahoney.

Hou is one of 48 newly elected Fellows, recognized specifically for his knowledge in 鈥渄emocratic design in the global built environment,鈥� elevating 鈥済rassroots activism for environmental equity and justice into the public, professional, and academic spheres.鈥澛燗 member of the 91探花Department of Landscape Architecture since 2001, Hou has worked in communities around the world, on projects from wildlife habitat conservation to urban open space design. In addition to his work with community members, Hou has edited, co-edited and co-authored 12 books, and he has written dozens of book chapters and journal articles. He also won the 2023 Outstanding Educator Award from the Council of Educators in Landscape Architecture.

An investiture ceremony for the ASLA Fellows is planned for the 2023 Conference on Landscape Architecture in October.

, 91探花 professor in the Paul G. Allen School of Computer Science & Engineering, .

The fellowship from the John D. and Catherine T. MacArthur Foundation comes with an $800,000 stipend, commonly known as the “genius grant,” for recipients to use as they see fit. The Chicago-based foundation announced the 25 fellows on Wednesday.

Choi uses to develop artificial intelligence systems that have the ability to reason and can understand the implied meanings in human language. AI often uses rules-based models, such as logic or probability. But Choi says that these rules are too rigid to make sense of the nuances that most people take for granted when they talk to each other.

“When I received the phone call from the Foundation, I thought they were going to ask me to do some consulting work,” Choi said. “My heart almost stopped beating when I heard ‘congratulations’ instead. This is such a great honor because there have been only two other researchers in the natural language processing field who have received this award.”

Choi is still working on the exact plans for the award, but hopes to use it to pursue impactful, though potentially risky, research ideas.

“Taking the road less traveled may seem exciting at first, but sustaining this path can be lonely, riddled with numerous roadblocks and disheartening at times,” Choi said. “This fellowship will power me up to go ahead and take that adventurous route.”

Choi has already made advancements in several areas to push the field of natural language processing forward. One example is combining both visual and text inputs for these systems. Traditionally, these models are trained solely with text inputs, but Choi has designed models with both text and image inputs that reinforce each other to better mimic how people acquire knowledge about the world.

In another line of work, Choi uses computational linguistics to help AI identify deceptive intent or sentiment in writing. For this project, the research team designed a method to automate accurate detection of fake online consumer reviews. Then Choi extended this work to include assessing news articles based on intent to deceive as well as categorizing the articles as “hoax,” “satire” or “trustworthy.”

Recently Choi’s team developed , a research prototype designed to make AI more ethically informed. When presented with a moral dilemma 鈥� such as ignoring a supervisor鈥檚 phone call during working hours 鈥� Delphi weighs in on whether the situation is OK. Choi led the Delphi project through a joint appointment at the .

In announcing the award, the MacArthur Foundation said, “Choi’s research brings us closer to computers and artificial intelligence systems that can grasp more fully the complexities of language and communicate accurately with humans.”

After receiving a doctoral degree from Cornell University, Choi was an assistant professor in the computer science department at SUNY Stony Brook before joining the Allen School faculty in 2014.

“The MacArthur Foundation could not have picked a better candidate than Yejin Choi,” said , 91探花professor and director of the Allen School. “Yejin epitomizes what the ‘genius grant’ is all about 鈥� she is fearless about breaking down barriers, asking hard questions and pushing AI in exciting new directions.”

“Natural language processing, and AI more broadly, have become deeply intertwined with all aspects of society,” Balazinska continued. “It is critical that we deeply understand its capabilities and limitations, and that we push those capabilities in the interest of social good. Yejin does exactly that, and she does so in such creative and sometimes astonishing ways. I can’t wait to see what she will accomplish next, thanks to this award.”

In winning the MacArthur Fellowship, Choi joins 13 others who were current 91探花 faculty at the time of their awards. The most recent winner was , affiliate associate professor of genome sciences and of epidemiology at the UW, who .

“We are delighted and proud that Yejin has received this significant acknowledgment of her innovative leadership in AI research,” said , Frank & Julie Jungers Dean of the 91探花College of Engineering. “Yejin is a creative, gifted computer scientist whose expertise in natural language processing is advancing AI systems’ capacities. We are eager to see how her work will further transform computer-human interactions and advance engineering for the public good.”

The fellowship is awarded, in the words of the Foundation, to “talented individuals in a variety of fields who have shown exceptional originality in and dedication to their creative pursuits.” Winners have been nominated anonymously by leaders in their fields and chosen by an anonymous selection committee.

“It鈥檚 been several weeks since I learned about this award, and it still feels so surreal,” Choi said. “I feel like the universe is sending me this message that it’s OK to be imperfect, it’s OK to try many ideas and fail along the way, as long as I continue learning and don’t give up.”

For more information, contact Choi at yejin@cs.washington.edu.

The 91探花team developed , a conversational agent designed to provide engaging and informative conversation and to transform how people interact with everyday devices in their homes. The team from the 91探花Department of Electrical Engineering and the Paul G. Allen School of Computer Science & Engineering took home the $500,000 first prize, which will be shared among the students.

Their challenge was to produce a socialbot 鈥� an AI agent capable of coherent conversation 鈥� that could converse about popular topics and current events for a goal of 20 minutes. Teams built their socialbots using the Alexa Skills Kit and received continuous, real-world feedback from millions of Amazon customers who interacted with teams anonymously through Alexa.

Amazon from three worldwide finalists on Tuesday at the conference in Las Vegas.

“Our philosophy in developing Sounding Board was to bring a variety of relevant content into a natural conversation,鈥� said team leader and electrical engineering doctoral student . 聽鈥淯ltimately, we hope Sounding Board can become a conversational gateway to online information that users enjoy talking with.鈥�

The Sounding Board socialbot earned an average score of 3.17 on a 5-point scale from a panel of independent judges and achieved an average conversation duration of 10:22.

The runner up team from Czech Technical University in Prague, which attained an average score of 2.72 and had an average conversation duration of 3:55, received a $100,000 prize. The third-place team聽from in Edinburgh, Scotland, received a $50,000 prize for an average score of 2.36 and an average conversation duration of 4:01.

The 91探花Sounding Board team combines expertise in natural language processing, speech technology and human-AI collaboration from additional team members EE doctoral student and Allen School doctoral students Elizabeth Clark, , and . EE professor is the lead faculty advisor for the team, working in collaboration with professors and of the Allen School鈥檚 Natural Language Processing research group.

鈥淭he students started from scratch, with no experience building a dialog system or working with Alexa skills, but together they brought a breadth of perspectives on language processing and a passion for understanding both the technical and human factors challenges of conversational AI,鈥� Ostendorf said.

The Sounding Board design is both user- and content-driven. The system aims to understand user comments in multiple dimensions, from directives to sentiment and personality, in order to best serve user interests. At the same time, the system relies on having interesting and timely things to talk about. It actively harvests online content and leverages a knowledge graph to provide connections between related topics that can be used to steer the conversation.

鈥淪ounding Board is unique in its ability to understand what type of person the user is, and is able to adjust parts of the conversation based on who it thinks the user is,鈥� said the Allen School鈥檚 Sap.

The 91探花team relied on the collaborative environment at the university, both for getting feedback on technical ideas and for user testing. Faculty and students from across the UW-NLP community 鈥� in computer science, electrical engineering and linguistics 鈥� provided input on the many different versions of Sounding Board as it evolved.聽 In addition, a key resource in system development was access to real Alexa users nationwide. “It is impossible to anticipate all the types of reactions and questions people will have, even the different ways that a simple yes-or-no question can be answered. Learning from actual user data is critical,鈥� Ostendorf said.

More than 100 teams from universities in 22 countries applied to be part of the inaugural competition. The finalists were selected from among 12 semifinalists whose socialbots were evaluated based on customer ratings of their interactions during hundreds of thousands of conversations last summer.

The three finalists continued to improve their socialbots by leveraging customer interactions through Nov. 7, and Amazon selected the winner based on assessments of a panel of judges listening to conversations with three interactors.

Amazon will publish technical papers from all participating teams in the Alexa Prize Proceedings as a way of sharing their work with the broader research community.

鈥淲e envision that conversational AI will be integral at the interface between humans and machines, and the Alexa Prize makes an important step toward that vision,鈥� said Choi. 鈥淚t has been an exciting journey to build Sounding Board, and we look forward to working on crucial research challenges that we have identified along the way.鈥�

But the two princesses actually exert very different levels of power and control over their own destinies, according to from 91探花 computer scientists.

The team used machine-learning-based tools to analyze the language in nearly 800 movie scripts, quantifying how much power and agency those scripts give to individual characters. In their study, recently presented in Denmark at the , the researchers found subtle but widespread gender bias in the way male and female characters are portrayed.

鈥溾�橣rozen鈥� is an interesting example because Elsa really does make her own decisions and is able to drive her own destiny forward, while Anna consistently fails in trying to rescue her sister and often needs the help of a man,鈥� said lead author and Paul G. Allen School of Computer Science & Engineering doctoral student , whose team also applied the tool to Wikipedia plot summaries of several classic Disney princess movies.

鈥淎nna is actually portrayed with the same low levels of power as Cinderella, which is a movie that came out more than 60 years ago. That鈥檚 a pretty sad finding,鈥� Sap said.

The team also created a searchable showing the subtle gender biases in hundreds of Hollywood movie scripts, which range from late 80s cult classics like 鈥淗eathers鈥� to romantic comedies like 鈥�500 Days of Summer鈥� to war films like 鈥淎pocalypse Now.鈥�

In their analysis, the researchers found that women were consistently portrayed in ways that reinforce gender stereotypes, such as in more submissive positions and with less agency than men.聽 For example, male characters spoke more in imperative sentences (鈥�Bring me my horse鈥�) while female characters tended to hedge their statements (鈥�Maybe I am wrong鈥�). However, the bias is not just in the words these characters speak, but also in the way they are portrayed through narratives.

To study the nuanced biases in narratives, the 91探花researchers expanded prior work presented in 2016 on 鈥溾�� that give insights into how different verbs can empower or weaken different characters through their connotative meanings. 聽The study evaluated the power and agency implicit in 2,000 commonly used verbs, where the connotative meanings were obtained from Amazon Mechanical Turk crowdsourcing experiments.

The power dimension denotes whether a character has authority over another character, while the agency dimension denotes whether a character has control over his or her own life or storyline. For each verb, turkers were asked to rank the implied level of power differentials and agency on a scale of 1 to 3.

鈥淔or example, if a female character 鈥榠尘辫濒辞谤别蝉鈥� her husband, that implies the husband has a stance where he can say no. If she 鈥榠苍蝉迟谤耻肠迟蝉鈥� her husband, that implies she has more power,鈥� said co-author , an Allen School doctoral student. 鈥淲hat we found was that men systematically have more power and agency in the film script universe.鈥�

Verbs that imply low power or agency include words like ask, experience, happen, wait, relax, need or apologize. Verbs that confer high power or agency include words like finish, prepare, betray, construct, destroy, assign or compose.

Using the movie scripts, the researchers automatically identified genders of 21,000 characters based on names and descriptions. Using natural language processing tools, which employ machine learning, they looked at which characters appeared as a verb鈥檚 subject and object. They then computed how much agency and power were ascribed to these characters, using their crowdsourced connotation frames. The researchers also accounted for the fact that male actors spent more time on screen than female actors and also spoke more, accounting for 71.8 percent of the words spoken across all movies.

The team calculated separate power and agency scores for male and female characters in each movie. They also created scores based on words that the characters spoke in dialogue and on words that were used in narration or stage direction to describe those characters 鈥� exposing subtle differences and biases.

In 2010鈥檚 鈥淏lack Swan,鈥� a movie centered around a female lead 鈥� a perfectionist ballerina who slowly loses grip on reality 鈥� the movie鈥檚 dialogue gives more agency to female characters. But the language used to describe the characters in stage direction and narration gave male characters more power and agency in that film.

In the 2007 movie 鈥淛uno,鈥� about an offbeat young woman who unexpectedly gets pregnant, male characters鈥� scene descriptions and narratives also consistently score higher in power and agency, though the two genders come closer in their dialogue.

The 91探花team鈥檚 tool yields a much more nuanced analysis of gender bias in fictional works than the , which only evaluates whether at least two female characters have a conversation about something other than a man.

The tendency for male characters to score higher on both power and agency dimensions held true throughout all genres: comedy, drama, horror, sci-fi, thrillers. Interestingly, the team found the same gender bias even for movies with female casting directors or script writers.

鈥淲e controlled for this. Even when women play a significant role in shaping a film, implicit gender biases are still there in the script,鈥� said co-author and Allen School doctoral student .

Next steps for the team include broadening the tool to not only identify gender bias in texts but also to correct for it by offering rephrasing suggestions or ways to make language more equal across characters of different genders. The methodology isn鈥檛 limited to movies, but could be applied to books, plays or any other texts.

鈥淲e developed this tool to help people understand how they may be perpetuating these subtle but prevalent biases that are deeply integrated in our language,鈥� said senior author , an associate professor in the Allen School. 鈥淲e believe it will help to have this diagnostic tool that can tell writers how much power they are implicitly giving to women versus men.鈥�

The research was funded by the National Science Foundation, Google and Facebook. The other co-author is former 91探花Allen School undergraduate .

For more information, contact the research team at debiasing-ai@cs.washington.edu.

Grant numbers: NSF: IIS 鈥� 1524371, NSF: IIS 鈥� 1714566, NSF: DGE – 1256082