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Understanding By Design

On Linguistic Imperialism

Authored by Dr. Safiya U. Noble, an assistant professor at the University of Southern California Annenberg School of Communication, this text is one of the preeminent works that explicitly addresse...

Algorithms of Oppression: How Search Engines Reinforce Racism

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---ie, what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities

/pdf/language-technology-is-power-a-critical-survey-of-bias-in-40xzysfmb0.pdf

Death is an inevitable part of life, but digital systems have been slow to address the complex issues that arise when a person passes away. As digital systems have become an increasingly important part people's lives, so too have these systems begun to play a role in a number of practices related to death, such as legacy-making, bereavement, and remembrance. In this paper, we conduct a survey of seventy-five digital systems designed to support these practices. Building on prior work, we analyzed these systems to derive findings that articulate how digital technologies reflect how people think about and engage with death. Based on these findings, we generated strategies designed to help system designers grapple with the complex needs that result from navigating death online.

https://dl.acm.org/doi/pdf/10.1145/2901790.2901802

Engaging with Death Online: An Analysis of Systems that Support Legacy-Making, Bereavement, and Remembrance

In this paper we focus on modeling friendships between humans as a way of working towards technology that can initiate and sustain a lifelong relationship with users. We do this by predicting friendship status in a dyad using a set of automatically harvested verbal and nonverbal features from videos of the interaction of students in a peer tutoring study. We propose a new computational model used to model friendship status in our data, based on a group sparse model (GSM) with L2,1 norm which is designed to accommodate the sparse and noisy properties of the multi-channel features. Our GSM model achieved the best overall performance compared to a non-sparse linear model (NLM) and a regular sparse linear model (SLM), as well as outperforming human raters. Dyadic features, such as number and length of conversational turns and mutual gaze, in addition to low level features such as F0 and gaze at task, were found to be good predictors of friendship status.

/pdf/automatic-prediction-of-friendship-via-multi-model-dyadic-tvb0vn207f.pdf

Automatic Prediction of Friendship via Multi-model Dyadic Features

People accumulate huge assortments of virtual possessions, but it is not yet clear how systems and system designers can help people make meaning from these large archives. Early research in HCI has suggested that people generally appear to value their virtual things less than their material things, but theory on material possessions does not entirely explain this difference. To investigate if changes to the form and behavior of virtual things may surface valued elements of a virtual archive, we designed a technology probe that selected snippets from old emails and mailed them as physical postcards to participating households. The probe uncovered features of emails that trigger meaningful reflection, and how contextual information can help people engage in reminiscence. Our study revealed insights about how materializing virtual possessions influences factors shaping how people draw on, understand, and value those possessions. We conclude with implication and strategies for aimed at supporting people in having more meaningful interactions and experiences with their virtual possessions.

Mailing Archived Emails as Postcards: Probing the Value of Virtual Collections

/pdf/towards-a-framework-for-smart-classrooms-that-teach-43sf6kkxad.pdf

Towards a Framework for Smart Classrooms that Teach Instructors to Teach

Teaching assistants (TAs) in the United States play a prominent role in educating undergraduates. Their influence can make the difference between students continuing in their majors or leaving them. However, most TAs use teacher-centered, transmission models of teaching, i.e., lecturing to disengaged students. Part of the reason for this is that most TAs receive little training on how to teach,and almost no grounded feedback about their teaching behaviors. In this thesis I describe my work investigating the use of technology to increase feedback and trainingfor TAs. My focus is understanding how their knowledge, skills, beliefs, and attitudes should drive thedesign of algorithms for gathering classroom behavioral data and delivering computer-mediated feedback and consultation. My work evaluates a novel framework for investigating how TAs interact with their data, reflect on what it means, and decide what (if anything) to change in their teaching. I examine how initial beliefs can impact their system interactions, how those beliefs change over time,and the resulting implications for designing data-driven training artifacts.

A Socio-Technical Approach to Feedback and Instructional Development for Teaching Assistants

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