Sara Elhawash

The Data Sciences Institute (DSI) at the University of Toronto is partnering with the United Nations Children’s Fund (UNICEF)’s Frontier Data and Tech team to find new ways to use data to address complex challenges involving children.

Through joint research and training, DSI – a �鶹ֱ��app institutional strategic initiative – will work with UNICEF to strengthen the international organization’s ability to use data science to drive innovation in learning.

That includes a partnership focused on using data and artificial intelligence, or AI, to predict student dropout rates and develop better early warning systems that inform decision makers in the development and humanitarian realm.

“This partnership is a significant milestone for our Frontier Data Network, a global community of practice that leverages data science to positively impact the lives of children worldwide,” says Yves Jaques, chief of the Frontier Data and Technology unit at UNICEF.

“Together, we are poised to unlock new insights, drive evidence-based decision-making, and pave the way to a brighter future for children everywhere.”

The effort aligns with DSI’s strategic goal of leveraging the potential of data to promote the greater public good.

As an initial collaboration, Zahra Shakeri, an assistant professor at the Institute of Health Policy, Management and Evaluation (IHPME) at the Dalla Lana School of Public Health, will partner with Manuel Garcia-Herranz, data principal researcher, and Karen Avanesyan, statistics and monitoring education specialist at UNICEF’s Division of Data, Analytics, Planning and Monitoring (DAPM) on a project that aims to revolutionize early warning systems in education through the application of cutting-edge AI technology.

As part of the 2023 Summer Undergraduate Data Science (SUDS) Opportunities Program, the project allows Ziqi Shu, a third-year student studying computer science, cognitive science and mathematics, to gain practical experience by working on fictional case studies focusing on social problems affecting children. By identifying at-risk students and schools with high dropout rates, UNICEF aims to support countries with a strong Education Management Information System (EMIS) and household survey data.

“Our aim is to develop a pilot tool that provides a comprehensive representation of the machine learning-based school dropout prediction landscape, bridging the knowledge gap in this area,” says Shakeri, who is also director of the Health Informatics, Visualization, and Equity (HIVE) Lab at IHPME. “This tool will utilize innovative data analysis and visualization techniques, benefiting researchers, practitioners and other stakeholders in exploring the factors influencing school dropout among children.

“The long-term goal of this project is to harness the power of data science and create an adaptable, publicly accessible system that could support countries in addressing the critical issue of school dropouts. By leveraging AI technology and early warning systems, our aim is to identify and support at-risk students and schools, ultimately safeguarding every child’s right to education.”

The UNICEF-DSI partnership paves the way for further research and training collaborations.

There will be opportunities to connect with the DSI community during its DSI Research Day on Sept. 27, where Garcia-Herranz will deliver the keynote address and Jaques will participate in a panel discussion on developing an effective data science workforce. The discussion will focus on equipping graduates with essential data science skills required in today’s diverse fields and industries. DSI Research Day aims to showcase the work of the DSI community, fostering connections and engagement among academia, industry and government stakeholders.

“By combining our community’s expertise in data science with UNICEF’s commitment to driving results for children globally, we have the opportunity to make a profound impact,” says Lisa Strug, director of the Data Sciences Institute.

“Through our joint efforts, we aim to strengthen UNICEF’s knowledge and capacities in utilizing data science methodologies, fostering innovation in learning and ultimately creating a brighter future for children worldwide.”

�鶹ֱ��app researchers developing AI system to tackle harmful social media content

Christopher.Sorensen — Fri, 12 May 2023 19:09:47 +0000

�鶹ֱ��app researchers developing AI system to tackle harmful social media content

Christopher.Sorensen Fri, 05/12/2023 - 15:09

Cutline

(photo by Chesnot/Getty Images)

Sara Elhawash

Topic

Our Community

Data Sciences Institute

Institutional Strategic Initiatives

Schwartz Reisman Institute for Technology and Society

Artificial Intelligence

Faculty of Arts & Science

Faculty of Information

Research & Innovation

Social Media

�鶹ֱ��app Scarborough

Hate speech and misinformation on social media can have a devastating impact, particularly on marginalized communities. But what if we used artificial intelligence to combat such harmful content?

That’s the goal of a team of University of Toronto researchers who were awarded a Catalyst Grant by the Data Sciences Institute (DSI) to develop an AI system to address the marginalization of communities in data-centric systems – including social media platforms such as Twitter.

The team consists of three faculty members. Syed Ishtiaque Ahmed is an assistant professor in the department of computer science in the Faculty of Arts & Science and a fellow of the Schwartz Reisman Institute for Technology and Society. Shohini Bhattasali is an assistant professor in the department of language studies at �鶹ֱ��app Scarborough. Shion Guha is an assistant professor cross-appointed between the department of computer science and the Faculty of Information, the director of the Human-Centered Data Science Lab and a faculty affiliate of the Schwartz Reisman Institute for Technology and Society.

Their goal is to make content moderation more inclusive by involving the communities affected by harmful or hateful content on social media. The project is a collaboration with two Canadian non-profit organizations: the Chinese Canadian National Council for Social Justice (CCNC-SJ) and the Islam Unravelled Anti-Racism Initiative.

Historically marginalized groups are most affected by content moderation failings as they have lower representation among human moderators and their data is less available for algorithms, Ahmed explains.

(L-R) Syed Ishtiaque Ahmed, Shohini Bhattasali and Shion Guha (supplied photos)

“While most social media platforms have taken measures to moderate and identify harmful content and limit its spread, human moderators and AI algorithms often fail to identify it correctly and take proper actions," he says.

The team plans to design and evaluate the proposed system to address potential Islamophobic and Sinophobic posts on Twitter. The AI system aims to democratize content moderation by including diverse voices in two primary ways: first, by allowing users to contest a decision, the moderation process becomes more transparent and trustworthy for users who are victims of online harms. Second, by taking user input and retraining machine learning (ML) models, the system ensures that users’ contesting positions reflect on the prescreening ML system.

“Annotating data becomes challenging when the annotators are divided in their opinions. Resolving this issue democratically requires involving different communities, which is currently not common in data science practices," Ahmed notes.

"This project addresses the issue by designing, developing and evaluating a pluralistic framework of justification and contestation in data science while working with two historically marginalized communities in Toronto.”

The AI system will integrate the knowledge and experiences of community members into the process of reducing hateful content directed toward their communities. The team is using a participatory data-curation methodology that helps them learn about the characterization of different kinds of harmful content affecting a community and includes members of the corresponding community in the data-labelling process to ensure data quality.

“We are grateful to DSI for their generous support for this project. The DSI community has also helped us connect with people conducting similar research and learn from them," Ahmed says, adding that his team's research is expected to have far-reaching impacts beyond the two communities it is currently focused on.

What's with those colours? Sean Thomas on the science behind this year's fall foliage

Christopher.Sorensen — Fri, 21 Oct 2022 13:01:16 +0000

What's with those colours? Sean Thomas on the science behind this year's fall foliage

Christopher.Sorensen Fri, 10/21/2022 - 09:01

Cutline

(photo by David Lee)

Topic

John H. Daniels Faculty of Architecture

Toronto residents are enjoying a particularly dazzling autumn display this year as the city’s tree canopy changes colour amid the cooling temperatures.

That includes the fiery red leaves of the sugar maple that stands on the southeast edge of Spadina Circle near the University of Toronto’s John H. Daniels Faculty of Architecture, Landscape, and Design, where Sean Thomas is a professor of forestry and a longtime associate editor at the journal Tree Physiology.

Thomas says a perfect storm of climatic conditions this season have produced especially vibrant leaf tones.

He recently spoke to Danny Sinopoli about the science behind this year’s eye-catching autumnal foliage.

Why do leaves change colour in the fall? What’s the science behind it?

There is a common misconception that autumnal leaf-colour change is due entirely to degradation of chlorophyll that “unmasks” other pigments that are already there. This is true basically with yellow colouration, which is mainly due to carotenoid pigments. However, red colours in fall foliage are due to newly produced anthocyanin pigments, which raises an intriguing functional biology question: What is the adaptive value in leaves producing new pigments just before the leaves are to be shed?

There are a couple of hypotheses, but the explanation that I think has received the most support is that anthocyanin pigments are playing a role as a “sunscreen” that better enables trees to recover nutrients from senescing leaves. It turns out that the breakdown products of chlorophyll are highly reactive, particularly under high UV exposure and low temperatures. Without the protection offered by anthocyanins, free radicals are generated from the breakdown products of chlorophyll that disrupt the process of nutrient recovery. Some anthocyanins are also antioxidants and scavenge the free radicals, so there are likely two aspects to their protective function during leaf senescence.

This nutrient recovery hypothesis predicts that anthocyanin production should be greatest when temperatures are low – but still above freezing, since frost events kill leaves – and light levels are high. This pattern is widely supported. Demonstrating that anthocyanin production actually increases nutrient recovery itself is more difficult to demonstrate, but there is some evidence for this as well.

So, are the colours actually more vivid this year?

Compared to last year, yes. Consistent with the theory, last fall was a relatively warm one in the GTA, without near-frost events until well into November – and also relatively cloudy conditions. This year had the right combination [for brighter colour] of cold weather events, lack of drought or a hard frost and relatively sunny conditions.

A sugar maple on Spadina Circle puts on a fall show (photo by Zheren Zheng)

Why do people react so strongly to red tones – like those of the sugar maple on Spadina Circle?

I think the accepted psychological theory is that red provokes strong emotional reactions because it is a danger cue. But what happens when the entire landscape is red, or the colour is rendered on such a large scale?

To speculate wildly outside of my area of expertise, a red forest landscape may initially provoke a kind of alarm reaction, to which viewers then acclimate, and this acclimation is pleasantly stimulating. Perhaps this is a bit like spicy food: “Hot” flavours are due to pain receptors, and the relaxation of the pain response releases endorphins.

Sara Elhawash

�鶹ֱ��app's Data Sciences Institute partners with UNICEF to drive innovation in learning

�鶹ֱ��app researchers developing AI system to tackle harmful social media content

What's with those colours? Sean Thomas on the science behind this year's fall foliage

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