Towards transparency in AI, Methods and Challenges
Date: Wednesday, April 17, 2019
Time: 4:00 PM to 5:00 PM
Location: 32-G449 (Stata Center - Patil/Kiva Conference Room)
Event Type: Seminar
Host: David Sontag, MIT
Contact: David Sontag, firstname.lastname@example.org
Speaker URL: http://ai.stanford.edu/~tgebru/
TALK: Towards transparency in AI, Methods and Challenges
Automated decision making tools are currently used in high stakes scenarios. From natural language processing tools used to automatically determine one’s suitability for a job, to health diagnostic systems trained to determine a patient’s outcome, machine learning models are used to make decisions that can have serious consequences on people’s lives. In spite of the consequential nature of these use cases, vendors of such models are not required to perform specific tests showing the suitability of their models for a given task. Nor are they required to provide documentation describing the characteristics of their models, or disclose the results of algorithmic audits to ensure that certain groups are not unfairly treated. I will show some examples to examine the dire consequences of basing decisions entirely on machine learning based systems, and discuss recent work on auditing and exposing the gender and skin tone bias found in commercial gender classification systems. I will end with the concept of AI datasheets for datasets and model cards for model reporting to standardize information for datasets and pre-trained models, in order to push the field as a whole towards transparency and accountability. Recently, we have seen many powerful entities in academia and industry announcing initiatives related to AI ethics. I will spend some time in this talk discussing how we can learn from the mistakes and evolutions of other disciplines who have/continue to perform what some call parachute research: one that uses the pain of marginalized communities without centering their voices or benefiting them.
Timnit Gebru is a research scientist in the Ethical AI team at Google and just finished her postdoc in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research, New York. Prior to that, she was a PhD student in the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Her main research interest is in data mining large-scale, publicly available images to gain sociological insight, and working on computer vision problems that arise as a result, including fine-grained image recognition, scalable annotation of images, and domain adaptation. She is currently studying the ethical considerations underlying any data mining project, and methods of auditing and mitigating bias in sociotechnical systems. The New York Times, MIT Tech Review and others have recently covered her work. As a cofounder of the group Black in AI, she works to both increase diversity in the field and reduce the negative impacts of racial bias in training data used for human-centric machine learning models.
AI & Machine Learning
Created by David A. Sontag at Thursday, April 11, 2019 at 9:58 AM.