Featured picture courtesy of the McGill Physics Hackathon
Primary areas of research focus:
Participatory design approaches to machine learning - designing asynchronous collaborative approaches leveraging participatory design methodology mediated through bot interfaces to build more inclusive AI systems.
Privacy – bridging the gap between the legal and technical domains to advise on privacy legislation amendments and assessing privacy implications of new technological solutions like contact- and proximity-tracing applications.
Disinformation – working on technical and design interventions to combat the spread of misinformation and disinformation related to climate change for the most susceptible Canadians. Surfacing language- and text-based signals to the UX and studying perceptual changes in users.
Labor Impacts of AI – studying on-the-ground impacts on the lives of blue- and white-collar workers due to automation. Designing iconic demonstrations to galvanize both workers and policymakers to take informed decisions to empower workers to transition into the future of work.
Machine Learning Security – increasing awareness and education for both cybersecurity and machine learning practitioners on the emergent concerns in machine learning security to protect the incoming and future generations of technological solutions using AI.
Environmental Impacts of AI – given that AI-enabled solutions are compute- and data-heavy, we are consolidating the fragmented reporting standards to inform developers and users to make informed choices in picking products and services that square with their environmental norms and values.
Indigenous Data Rights – respecting Traditional Knowledge and data sovereignty requirements, designing a framework to meaningfully incorporate indigenous data into building AI solutions that are truly inclusive .
Other areas of research, application, and community engagement
- Labor shifts in the financial sector: Labor impacts of AI-enabled automation in the financial services industry in Canada.
- AI Ethics in Education: Introductory curriculum via a 4-part workshop series to introduce the topic of the ethical development of AI to students, faculty, staff and alumni of the McGill community.
- AI Ethics in Medicine: Informed consent and its ethical implications in the field of medicine, especially when AI-enabled solutions are used in diagnosis and clinical trials.
- Mission-driven AI: research on how non-profits, social enterprises and NGOs can leverage machine learning solutions to help them stretch their donation dollars further by scaling the work that they do and increasing efficiency of their operations
- Social inclusion in AI: Technical and policy approaches to increase social inclusion in the entire pipeline of AI - from design and conception, data collection and use all the way to the end-of-life management of a project.
- AI and Law: Legal and ethical implications with the increasing use of AI in the context of emerging privacy and data security laws like the GDPR
If any one of the above areas interest you or you’re interested in collaborating on the intersection of AI with another field and exploring its ethical implications, feel free to reach out to me via email or any other channel as linked in the bottom section.