Sina Fazelpour (MSc 1T0, BA 1T3)
How should we understand and model our world and design technologies that work effectively in it? What should we do to ensure that our diverse societies are just and inclusive? In this talk, I will discuss my personal journey from biomedical engineering to philosophy, a journey that was instigated by personal and social events, and driven by an interest in better understanding these two broad sets of questions. I will discuss how these two sets of questions come together when we think about our practices of modeling and technological design. Using examples from computational modeling, AI and digital technologies, I will explain why ethics and politics can be inextricably linked to the practice of engineering, and the responsibilities that this connection brings.
Read more about Sina Fazelpour here and submit a question to him before the conference.
Years 1 and 2 EngSci students: T-cards will be scanned to take attendance.
Deb Raji (Year 4 EngSci + PEY) and researchers at the MIT Media Lab identified a need for stronger evaluation practices to mitigate gender and racial biases of AI products. (Credit: Liz Do)
As artificial intelligence (AI) software becomes more widely used, questions have arisen about how social biases may inadvertently be amplified through it. One area of concern is facial detection and recognition software. Biases in the data sets used to ‘train’ AI software may lead to racial biases in the end products. Since these are sometimes used in law enforcement, this raises civil rights concerns.
Year 4 EngSci student Deb Raji (1T8 PEY) and collaborators at the Massachusetts Institute of Technology (MIT) recently won “best student paper” at the Artificial Intelligence, Ethics, and Society (AIES) Conference in Honolulu, Hawaii, for identifying performance disparities in commonly used facial detection software when used on groups of different genders and skin tones. Using Amazon’s Rekognition software, they found that darker-skinned women were misidentified as men in nearly one-third of cases.
Raji hopes that this work will show companies how to rigorously audit their algorithms to uncover hidden biases. “Deb Raji’s work highlights the critical need to place engineering work within a social context,” says Professor Deepa Kundur, Chair of the Division of Engineering Science. “We’re very proud of Deb’s achievements and look forward to her future contributions to the field.”
Read about Raji’s research here.