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Add to Calendar 3/8/21 22:00 3/8/21 23:45 UTC Paper Session 8 Check out this session on the FAccT Hub.
Track Two

Paper Session 8

Session Chair:
Vinodkumar Prabhakaran
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Designing an Online Infrastructure for Collecting AI Data From People With Disabilities

Joon Sung Park, Danielle Bragg, Ece Kamar, Meredith Ringel Morris
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AI technology offers opportunities to expand virtual and physical access for people with disabilities. However, an important part of bringing these opportunities to fruition is ensuring that upcoming AI technology works well for people with a wide range of abilities. In this paper, we identify the lack of data from disabled populations as one of the challenges to training and benchmarking fair and inclusive AI systems. As a potential solution, we envision an online infrastructure that can enable large-scale, remote data contributions from disability communities. We investigate the motivations, concerns, and challenges that people with disabilities might experience when asked to collect and upload various forms of AI-relevant data through a semi-structured interview and an online survey that simulated a data contribution process by collecting example data files through an online portal. Based on our findings, we outline design guidelines for developers creating online infrastructures for gathering data from people with disabilities.

Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation

Hong Shen, Wesley H. Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu
View Paper


Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency and Ethics (FATE). In this paper, we present Value Cards, an educational toolkit to inform students and practitioners the social impacts of different machine learning models via deliberation. This paper presents an early use of our approach in a college-level computer science course. Through an in-class activity, we report empirical data for the initial effectiveness of our approach. Our results suggest that the use of the Value Cards toolkit can improve studentsโ€™ understanding of both the technical definitions and trade-offs of performance metrics and apply them in real-world contexts, help them recognize the significance of considering diverse social values in the development and deployment of algorithmic systems, and enable them to communicate, negotiate and synthesize the perspectives of diverse stakeholders. Our study also demonstrates a number of caveats we need to consider when using the different variants of the Value Cards toolkit. Finally, we discuss the challenges as well as future applications of our approach.

Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, Margaret Mitchell
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Rising concern for the societal implications of artificial intelligence systems has inspired demands for greater transparency and accountability. However the datasets which empower machine learning are often used, shared and re-used with little visibility into the processes of deliberation which led to their creation. Which stakeholder groups had their perspectives included when the dataset was conceived? Which domain experts were consulted regarding how to model subgroups and other phenomena? How were questions of representational biases measured and addressed? Who labeled the data? In this paper, we introduce a rigorous framework for dataset development transparency which supports decision-making and accountability. The framework uses the cyclical, infrastructural and engineering nature of dataset development to draw on best practices from the software development lifecycle. Each stage of the data development lifecycle yields a set of documents that facilitate improved communication and decision-making, as well as drawing attention the value and necessity of careful data work. The proposed framework is intended to contribute to closing the accountability gap in artificial intelligence systems, by making visible the often overlooked work that goes into dataset creation.

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