Congratulations to our lab director Dr. Chirag Shah for this milestone recognition

Congratulations to our lab director Dr. Chirag Shah for this milestone recognition

Chirag Shah

ACM honors iSchool’s Shah as Distinguished Member

UW Information School Professor Chirag Shah is among 67 global scholars recognized this year as Distinguished Members of the Association for Computing Machinery (ACM) for their outstanding contributions to the computing field. 

The ACM is the world’s largest computing society. It recognizes up to 10 percent of its worldwide membership as distinguished members based on their professional experience, groundbreaking achievements and longstanding participation in computing. The ACM has three tiers of recognition: fellows, distinguished members and senior members. The Distinguished Member Recognition Program, which honors members with at least 15 years of professional experience, recognized Shah for his work at the intersection of information, access and responsible AI. Shah expressed his gratitude and appreciation for the award. 

“I’m incredibly grateful for all the support I’ve received from everyone. It’s a very humbling experience,” said Shah. 

Shah has contributed a great deal of research related to people-centered information access and examining how biases and issues of discrimination that are present within information systems can be counteracted. One of Shah’s significant contributions to the iSchool has been co-founding Responsibility in AI Systems and Experiences (RAISE).

Read more: https://ischool.uw.edu/news/2022/12/acm-honors-ischools-shah-distinguished-member?fbclid=IwAR10RqqU2ltZZ4P-OFlZUjlc1i62UYy1TgzlRRwJ5cqNzUtGZTVBP1O_TGY

Our Lab Director quoted in: Why Meta’s latest large language model survived only three days online

Our Lab Director quoted in: Why Meta’s latest large language model survived only three days online

space bears floating in space

“I am both astounded and unsurprised by this new effort,” says Chirag Shah at the University of Washington, who studies search technologies. “When it comes to demoing these things, they look so fantastic, magical, and intelligent. But people still don’t seem to grasp that in principle such things can’t work the way we hype them up to.”

Red Full Article: https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/

No quick fix: How OpenAI’s DALL·E 2 illustrated the challenges of bias in AI

No quick fix: How OpenAI’s DALL·E 2 illustrated the challenges of bias in AI

Photo illustration of warped faces on scanned paper.

An artificial intelligence program that has impressed the internet with its ability to generate original images from user prompts has also sparked concerns and criticism for what is now a familiar issue with AI: racial and gender bias. 

And while OpenAI, the company behind the program, called DALL·E 2, has sought to address the issues, the efforts have also come under scrutiny for what some technologists have claimed is a superficial way to fix systemic underlying problems with AI systems.

“The common thread is that all of these systems are trying to learn from existing data,” Shah said. “They are superficially and, on the surface, fixing the problem without fixing the underlying issue.”

Read More: https://www.nbcnews.com/tech/tech-news/no-quick-fix-openais-dalle-2-illustrated-challenges-bias-ai-rcna39918?fbclid=IwAR3YkLgofPftAuQ0A-qKpRL1AOgXQkJqBtyMgk3iYNYkWNvMpaEzJ1obW1c

How AI Will Change IT Jobs?“

How AI Will Change IT Jobs?“

Ai vs. Humans

AI is not going to take away our jobs, but it’s going to change the landscape of opportunities,” – Dr. Chirag Shah

Read what our lab director has to say about AI taking over our jobs and if we should be concerned with technology making our jobs less meaningful and relevant. https://www.informationweek.com/team-building-and-staffing/how-ai-will-change-it-jobs?fbclid=IwAR2x-_zl0HRX_XnryvJUrVXsgR6E6zzDBR-o7Edg53AIVRj_m0NkH5skAeA

Predictive analytics in marketing: Achieving success

Predictive analytics in marketing: Achieving success

Predictive Analytics in Healthcare: A 4-Step Framework

Marketing and analytics experts said marketers can choose from a number of off-the-shelf predictive analytics tools with machine learning and AI built in. However, Shah explained that the more advanced marketing operations often build their own algorithms and custom tools, seeing it as a way to differentiate their efforts and maximize the success for their own organizations. “It almost also becomes a proprietary thing. For many companies, the way they derive their insights is the ‘secret sauce,'” he said.

Read More: https://searchbusinessanalytics.techtarget.com/…/Predic..

Has CEO Gender Bias really been fixed?

Has CEO Gender Bias really been fixed?

Dr. Yunhe Feng a fellow Infoseeking lab member and his team attended the Discovering AI@UW event co-hosted by the eScience Institute last week and presented his gender bias in image search work at the poster session.

This inspirational and educational event is a great networking opportunity for faculty, researchers, and students who are interested in using AI, finding synergies with major campus initiatives, and strategizing about the future of AI research at UW. 

ExpScore: Learning Metrics for Recommendation Explanation

ExpScore: Learning Metrics for Recommendation Explanation

Another published paper from our Lab members!

ABSTRACT
Many information access and machine learning systems, including recommender systems, lack transparency and accountability. Highquality recommendation explanations are of great significance to enhance the transparency and interpretability of such systems. However, evaluating the quality of recommendation explanations is still challenging due to the lack of human-annotated data and benchmarks. In this paper, we present a large explanation dataset named RecoExp, which contains thousands of crowdsourced ratings of perceived quality in explaining ecommendations. To measure explainability in a comprehensive and interpretable manner, we propose ExpScore, a novel machine learning-based metric that incorporates the definition of explainability from various perspectives (e.g., relevance, readability, subjectivity, and sentiment polarity). Experiments demonstrate that ExpScore not only vastly outperforms existing metrics and but also keeps itself explainable. Both the RecoExp dataset and open-source implementation of ExpScore will be released for the whole community. These resources and our findings can serve as forces of public good for scholars as well as recommender systems users.

Bingbing Wen | University of Washington, Seattle, WA, US | bingbw@uw.edu

Yunhe Feng | University of Washington, Seattle, WA, US| yunhe@uw.edu

Yongfeng Zhang | Rutgers University, New Brunswick, NJ, US | yongfeng.zhang@rutgers.edu
Chirag Shah | University of Washington, Seattle, WA, US, chirags@uw.edu |

Full article: https://dl.acm.org/doi/pdf/10.1145/3485447.3512269

Shruti Phadke’s Student Presentation Showcase: Understanding online communities of problematic information.

Shruti Phadke’s Student Presentation Showcase: Understanding online communities of problematic information.

Shruti Phadke is one of our lab members who is currently a Ph.D. candidate studying in Information School. Her presentation focuses on natural language processing computational methods and combining them with theories from social psychology to understand various Community practices in communities of problematic information.

Watch her presentation here: https://washington.zoom.us/rec/play/_NVHNS1USjeOks5XKrDwplJCvOU0Z1V83w0ah3B6PYIaew0QB9_4iJURaDTf9zz0df_ZR4ueuLPRV9Ll.DCvrb9IPF_bazC2x?continueMode=true

Authentic versus synthetic: An investigation of the influences of study settings and task configurations on search behaviors

Authentic versus synthetic: An investigation of the influences of study settings and task configurations on search behaviors

Abstract

In information seeking and retrieval research, researchers often collect data about users’ behaviors to predict task characteristics and personalize information for users. The reliability of user behavior may be directly influenced by data collection methods. This article reports on a mixed-methods study examining the impact of study setting (laboratory setting vs. remote setting) and task authenticity (authentic task vs. simulated task) on users’ online browsing and searching behaviors. Thirty-six undergraduate participants finished one lab session and one remote session in which they completed one authentic and one simulated task. Using log data collected from 144 task sessions, this study demonstrates that the synthetic lab study setting and simulated tasks had significant influences mostly on behaviors related to content pages (e.g., page dwell time, number of pages visited per task). Meanwhile, first-query behaviors were less affected by study settings or task authenticity than whole-session behaviors, indicating the reliability of using first-query behaviors in task prediction. Qualitative interviews reveal why users were influenced. This study addresses methodological limitations in existing research and provides new insights and implications for researchers who collect online user search behavioral data.

Authors: Yiwei Yang, Chirag Shah

Read full journal: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24554