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Management Information Systems

4 Mitacs Globalink (GRI) research projects for Summer 2027.

1. Auditing Recommender Systems for bias towards Content Creators

Recommender systems significantly influence content creators' visibility and livelihoods, yet their design often prioritizes user metrics over creator needs. This can lead to perceptions of unfairness, a lack of control, and potential burnout within the creator economy. This project directly supports a larger research program aimed at identifying and mitigating biases towards creators and developing more equitable and transparent recommender systems. The student will assist in two critical early phases: auditing existing algorithms in different platforms using sockpuppet auditing and drafting a conceptual framework for creator-centric fairness and agency by analyzing the audit results, and existing fairness measures. This work is designed to refine definitions of fairness from the creators' perspective and to brainstorm tools and mechanisms that would provide them with meaningful agency. The primary goal of this project is to provide foundational support for understanding and operationalizing creator needs in recommender system design. Specific objectives include: Assisting in the synthesis of literature to contribute to Persona Definition: designs unique, automated bots (sock puppets) and assigns them highly specific "viewing" or "browsing" histories for creator-centric fairness and agency. Behavioral Simulation: Develop automated bots perform controlled actions—such as liking, sharing, or spending a specific amount of time on a video—to teach the platform's algorithm their supposed preferences. Data Logging: The audit system scrapes different feeds to see how content recommendations evolve, comparing unpersonalized and personalized feeds. This project will employ a sock puppet algorithm audit. The student will engage in: Define sockpoppet personas based on literature and diversity of Canadian creators and Audience addressing biases affecting under represented creators with focus on racial and linguistic discoverability Supporting implementation of sockpoppet audit on digital media platforms. Conduct Sockpuppet audit of platforms such as youtube and spotify under supervision. Assisting in literature review and documenting and analyzing audit data.

Research area, student roles & skills

Research area: Dr. Afsoon Soudi is an Assistant Professor at Toronto Metropolitan University and the Associate Director of Creative AI hub at The Creative School. She led machine intelligence teams to develop an audience analytics platform and recommender systems enabling discoverability and personalization in digital media products. Dr Soudi completed her Ph.D. in Physics at Washington State University. She holds multiple patents and published numerous peer-reviewed papers in high-impact journals. Her current research interests intersect responsible machine learning, sustainability and media.

Student roles:
This role offers a unique opportunity for hands-on research experience. The student will:

Gain a deep understanding of current research challenges in information retrieval, recommender systems, human-computer interaction (HCI), and the socio-technical aspects of the creator economy.
Acquire practical experience with research methodologies, specifically algorithmic auditing, persona development.
Enhance critical thinking skills through engagement with conceptual framework development.
Improve organizational, communication, and teamwork skills within a research environment.
Gain insight into the ethical considerations involved in designing technology with multiple stakeholders.
The undergraduate students will act as a Research Assistant providing vital support to the project. Working under the guidance of senior researchers, the student's responsibilities will include:
Summarizing and synthesizing research articles and reports related to fairness, agency, recommender systems, and the creator economy.
Contributions to Persona Definition: designs unique, automated bots (sock puppets) and assigns them highly specific "viewing" or "browsing" histories.
Assisting with the organization and analysis of audit data.
Assisting in organizing, visualizing, and interpreting preliminary findings from persona development and algorithm audit on media platforms.
Maintaining meticulous records of analyzed content, data sources, and analytical steps.
Participating in regular team meetings, contributing to methodological discussions, and potentially presenting initial findings to the research group.
Potential Deliverables will include:
A documented framework with creator persona based on Canadian media diversity
A cleaned and organized dataset of results from audie of algorithm for each persona
A preliminary report summarizing and quantifying the biases affecting discoverability of content creators in Canada based on audit data , co-developed with the research team.

Skills required:
While prior experience in academic research is not mandatory, we are looking for a motivated student who possesses the following qualities:

Excellent research and critical thinking skills, experience with academic writing is an asset.
Knowledge of web scrapping for behaviour simulation, and managing github repository
Knowledge of recommender systems is a plus
Effective communication skills: The ability to communicate the research findings clearly and effectively both written and orally both technical and non-technical audiences
Time Management: Proper time management skills to meet deadlines and complete the study within the specified timeline.
Strong interest in responsible machine learning, computational social science

2. Building a Secure EV Physical Testbed: EV Charging Infrastructure Setup

This project establishes the physical foundation of a Secure EV Lab. The intern(s) will design and deploy a fully functional EV charging testbed using Raspberry Pi computers to emulate the key components of a real EV charging ecosystem: an Electric Vehicle Communication Controller (EVCC), a Supply Equipment Communication Controller (SECC), and a Charging Station Management System (CSMS). The testbed will implement the ISO 15118 communication standard between the simulated EV (EVCC node) and the charging station (SECC node), and the OCPP protocol between the CSMS and the charging station. A wattmeter connected via the I2C bus will capture real-time power consumption data, feeding a power monitoring Raspberry Pi node. A dedicated network monitor node will log all traffic for security and analytics research. This testbed directly mirrors the architecture depicted in published EV charging security research and will serve as the shared experimental platform for the other internships (Projects 2, 3, and 4) once operational.

Research area, student roles & skills

Research area: Embedded systems, EV communication protocols (ISO 15118, OCPP), power electronics monitoring, and physical testbed design for smart charging infrastructure research.

Student roles:
Weeks 1–2: Setup & Literature Review
• Review ISO 15118 and OCPP protocol documentation
• Procure and inventory all hardware components
• Install OS and configure network environment on all Raspberry Pi nodes
Weeks 3–5: Core Testbed Construction
• Implement and test EVCC–SECC ISO 15118 communication stack
• Deploy OCPP interface between SECC and CSMS
• Integrate wattmeter and validate I2C power data collection
Weeks 6–7: Validation & Handoff
• Run full end-to-end charging session simulations
• Document testbed architecture, configuration scripts, and data APIs
• Brief companion intern teams on testbed capabilities and data access
Weeks 8–12: Support & Extended Research
• Provide ongoing support to companion project teams using the testbed
• Explore extensions: V2G (vehicle-to-grid) simulation, additional charging protocols
• Prepare technical report and contribute to joint research publication

Skills required:
Academic Background: Electrical Engineering, Computer Engineering, Computer Science, or related field. Embedded systems, Linux, Raspberry Pi or similar SBC, Python or C/C++, basic networking (TCP/IP)

3. Framing AI: Computational Analysis of Artificial Intelligence Discourse in the Canadian Parliament

This project investigates how artificial intelligence and digital technologies have been discussed, debated, and framed in the Canadian Parliament over the past years. Using the Open Parliament dataset, which contains the full text of House of Commons and Senate proceedings, we will build a computational pipeline to analyze how parliamentary discourse around AI has evolved. The core research question is: how have Canadian parliamentarians framed AI across time, and what does that tell us about the institutional drivers of AI governance? We approach this through framing analysis, a method that identifies not just what gets said about a topic, but how it gets constructed: as an economic opportunity, a rights concern, a national security issue, a labour disruption, or a threat to democratic integrity, etc. The student will work on all stages of the research pipeline. This includes constructing and cleaning the corpus from the Open Parliament API, developing and validating a framing codebook through collaborative human coding, training and evaluating a text classifier using transformer-based NLP models, and conducting longitudinal analysis to track how dominant frames shifted across parliamentary sessions and party lines. The project sits at the intersection of information systems, computational social science, and AI governance. It is well suited for a student with strong Python skills and experience in NLP or machine learning who is interested in applying those skills to a socially meaningful research question. No prior knowledge of Canadian politics is required, and the student will receive full methodological mentorship throughout. The expected outputs are a validated framing dataset covering Canadian parliamentary AI discourse, a set of longitudinal visualizations, and a co-authored paper draft targeted at an IS or computational social science venue. As governments worldwide race to regulate AI, understanding how democratic institutions construct meaning around these technologies is essential for building governance

Research area, student roles & skills

Research area: My research examines how digital technologies and AI shape individual and societal outcomes. I use computational methods including agent-based modeling and NLP techniques to study how AI-driven technologies influence decision-making across multiple levels of analysis. As an assistant professor of Information Technology Management at Toronto Metropolitan University, my work spans digital sustainability, generative AI, and technology governance. My current research extends into how AI is institutionally framed and regulated, examining the evolution of AI discourse in the Canadian Parliament through longitudinal computational text analysis.

Student roles:
The student will play a central role in all stages of the research pipeline, working closely with the supervisor throughout the internship. The position combines technical development with applied research, giving the student hands-on experience in both computational methods and academic knowledge production.

1- In the first phase, the student will retrieve and process parliamentary text data from the Open Parliament API, build a clean and well-documented corpus, and conduct initial descriptive analysis of AI-related discourse across parliamentary sessions. This includes designing and refining the keyword search strategy used to identify relevant speeches and interventions.

2- In the second phase, the student will collaborate with the supervisor to develop a framing codebook, read and manually annotate a sample of parliamentary speeches, and participate in inter-rater reliability testing to validate the coding scheme.

3- In the third phase, the student will implement and evaluate NLP models for automated frame classification across the full corpus. This includes experimenting with topic modeling approaches and transformer-based classifiers, evaluating model performance, and iterating based on validation results.

4- In the fourth phase, the student will conduct longitudinal and actor-level analysis, produce visualizations, and contribute to interpreting findings in the context of AI governance and parliamentary discourse.

Throughout the internship, the student will participate in weekly research meetings, maintain documented and reproducible code, and contribute to writing the methods and results sections of a research paper. The internship is designed to result in a co-authored publication, giving the student a concrete and competitive output for their academic or industry career.

Skills required:
The ideal candidate has good programming skills in Python and hands-on experience with natural language processing, including text preprocessing, topic modeling, and transformer-based classification models. Familiarity with libraries such as scikit-learn, HuggingFace Transformers, or BERTopic is an asset. The student should be comfortable working with large unstructured text datasets and have a basic understanding of machine learning evaluation metrics. A background in computer science, data science, or a related field is expected. Interest in social applications of NLP is an advantage but not a requirement. No prior knowledge of Canadian politics is needed.

4. Trust and Accountability in Human–Embodied AI Collaboration

As AI moves from screens into physical bodies (e.g., service robots, social robots, embodied assistants), a basic question becomes pressing: does giving an AI a body change how people trust it, and how they assign responsibility when something goes wrong? Disembodied chatbots and physically embodied agents may elicit very different patterns of trust calibration, anthropomorphism, and accountability attribution, with direct implications for the design and adoption of consumer-facing and collaborative robots. This project investigates how embodiment shapes trust and accountability in human–AI collaboration. Working in the Human–AI Collaboration (HAIC) Research Lab, the intern will use a physical robot platform together with semi-structured interviews and behavioural experiments to test how the form an AI takes may affects users' trust, their willingness to delegate consequential tasks, and how they distribute blame between the AI, its human operator, and the wider system after an error or failure. Over the 12 weeks the intern will: (1) review the relevant HRI, trust, and responsibility-attribution literatures; (2) help design and pilot a controlled between-subjects experiment manipulating AI embodiment; (3) collect data using the lab's robot platform and/or an online panel (e.g., Prolific); (4) analyze the results with standard quantitative methods; and (5) co-draft a short working paper or conference submission. The intern gains hands-on experience across the full empirical pipeline from theory to data to write-up in an active behavioural research lab, collaborating with researchers from various disciplines like engineering, computer science, psychology, and management.

Research area, student roles & skills

Research area: My research examines how people perceive, trust, and collaborate with AI and robotic systems, combining perspectives from consumer behaviour, social psychology, and human–computer/human–robot interaction (HCI/HRI). I study how design factors (e.g., an agent's embodiment, communication style) may shape user trust, anthropomorphism, psychological ownership, and the way responsibility is attributed when systems succeed or fail. I use controlled behavioural experiments, online panel studies, survey measures, and computational text analysis. The goal is to inform the responsible design and adoption of consumer-facing and collaborative AI and robotic technologies.

Student roles:
The student will be an active contributor to a research project examining how an AI system's embodiment shapes user trust and accountability attribution in human–AI collaboration. They will be involved across the full research pipeline and supervised closely throughout. In the first weeks, the student will conduct a focused literature review on trust in automation, human–robot interaction, anthropomorphism, and responsibility attribution, and will help refine the study's research questions and hypotheses. Working with the supervisor, they will then help design a controlled between-subjects experiment that manipulates the form of an AI agent (physical robot, on-screen agent, or voice-only) and measures participants' trust, willingness to delegate tasks, and how they attribute responsibility following a system error. Next, the student will help prepare experimental materials and stimuli, including configuring interaction scenarios on the lab's physical robot platform and/or building an equivalent online version. They will run a pilot study, refine the protocol, and then collect the main dataset using the robot platform and/or an online participant panel such as Prolific. In the analysis phase, the student will clean and organize the data, conduct statistical analyses under supervision, and help interpret the findings. They will contribute to a short working paper or conference submission, including drafting sections and preparing figures. Throughout the internship, the student will take part in regular lab meetings and one-on-one supervision, receive training on research ethics and data handling, and develop transferable skills in experimental research, quantitative analysis, scientific writing, and human–AI interaction. The role is designed to give a motivated undergraduate a complete, hands-on research experience suitable for someone considering graduate study.

Skills required:
The ideal student has a background in psychology, marketing, human–computer interaction, cognitive science, design, or a related social-science or interdisciplinary field. They should be familiar with the basics of experimental design and quantitative data analysis, with working knowledge of at least one statistical tool (R, SPSS, SAS, Python, etc.). Strong written and spoken English, attention to detail, and the ability to work independently are essential. Curiosity about AI, robots, and human behaviour is important. Prior experience with human–robot interaction, programming, or running studies on online panels (e.g., Prolific) is an asset but not required.