School of Information

The paper will investigate the complexities involved in moderating casteist content on major social media platforms. Focusing on the challenges of categorizing hate speech targeting India's marginalized caste-oppressed minorities, the research aims to delve into the mechanisms utilized by platforms for content moderation. It will critically analyze existing categorization systems, exploring the efficacy and limitations in identifying and addressing casteist comments. By leveraging information sciences concepts like grounded coding and taxonomies, the study will elucidate the complexities of classifying caste-related hate speech within these systems. Moreover, it will highlight the implications of inadequate categorization on content visibility and community impact.

The internet's key points of global control lie in the hands of a few people, primarily private organizations based in the United States. These control points, as they exist today, raise structural risks to the global internet's long-term stability. I argue: the problem isn't that these control points exist, it's that there is no popular governance over them. I advocate for a localist approach to internet governance: small internets deployed on municipal scales, interoperating selectively, carefully, with this internet and one another.

Visual data exploration enables analysts to identify trends and patterns, generate and verify hypotheses, and detect outliers and anomalies. However, the overwhelming number of decisions required in visual data exploration presents a barrier to discovering useful, action-able insights from data. To address this challenge, in this dissertation, we investigate how automated assistance via tooling aids visual data exploration.We introduce four systems to survey the design space of visual exploration assistants across different analytical tasks and interface modalities. We first describe VisPilot and Zenvisage++, two novel visual exploration assistants that accelerate the data exploration process for individual visual analysis tasks: drill-down analysis and pattern search. Next, we examine visual exploration assistants aimed at supporting multiple types of visual analysis tasks. We introduce Frontier, a general-purpose visual exploration assistant within a GUI-based charting tool that recommends potential next steps in a mixed-initiative visual analysis workflow. We further develop Lux, a general-purpose visual exploration assistant situated within a computational notebook that provides proactive, always-on recommendations within an exploratory programming workflow. Findings from this dissertation contribute towards designing an intelligent visual exploration assistant that suggests helpful tailored feedback based on user’s analytical needs and seamlessly guides users towards data-driven insights.

Recognizing the prevalence of initiatives to align technology with social values through design and “by design” (such as privacy by design, security by design, and governance by design), this dissertation explores the current and potential role of design techniques in attending to values, and analyzes user experience (UX) professionals’ “values work” practices—practices used to surface, advocate for, and attend to values—within large technology companies.

            The first part of the dissertation interrogates the relationship between values and design practices, looking at privacy as a case study. A review of human computer interaction literature about privacy and design suggests the importance of thinking about the purpose of design, who does the work of design, and on whose behalf is design work done. In order to better understand how design in the service of “values work” could be used towards purposes of exploration, critique and speculation, I create a set of speculative design fictions depicting a range of fictional products that suggest different sets of privacy harms. These designs serve as way to surface and foster reflection on values. The success of this design intervention in a laboratory setting sparked interest in understanding whether and how design approaches were used in values work within the technology industry.

            The second part of the dissertation seeks to understand the practices and strategies of UX professionals who already see addressing values as a part of their practice. I conducted interviews with UX professionals working at large technology companies, and field observations at meetups in the San Francisco Bay Area about technology design and values. These UX professionals report doing values work as a part of everyday configurations of UX work, such as when designing interfaces or conducting user research. More strikingly, UX professionals also report on engaging in a range of other activities aimed at shaping the organization, rather than a technical product or system. These practices are used by UX professionals to re-configure how values work is conducted at their organizations in several ways: by making more space for UX professionals’ values work; by getting others in the organization to adopt human-centered perspectives on values; and by changing the politics and strategies of the organization regarding values. Moreover, UX professionals’ values work practices occur within relations and systems of power. UX professionals often engage in tactics of soft resistance, seeking to subtly subvert existing practices towards more values-conscious ends while maintaining legibility as conducting business-as-usual within the organization. Together, these values work practices create social and organizational infrastructures to promote an alternative sociotechnical imaginary of large technology companies in a way that views these companies and their workers as more cognizant, proactive, and responsible for identifying and addressing social values, in particular reducing harms to users and other stakeholders.

The last part of the dissertation reflects on the politics of using speculative design techniques in the service of values work. Experiences sharing speculative designs with others who interpreted the designs in ways that do not recognize their speculative, critical, and reflective nature, raises questions about how speculative design can be re-appropriated by or co-opted towards the very ends that are being critiqued and reflected upon. One approach to this dilemma might be to conduct speculative design work with and for specific groups of stakeholders, instead of for broad public discussion. Another approach might be to create organizational fictions that focus a designer’s and viewer’s attention more on practices and social relationships, compared to traditional speculative designs that focus attention on fictional products. Informed by the practices of UX professionals involved in values advocacy, the dissertation concludes by suggesting a new purpose for design, design for infrastructuring imaginaries, to complement the social practices of values advocacy. I reflect on the politics of choosing design as a mode of action when conducting values work, and reflect on implications that this work has for values in design researchers, practitioners, and stakeholders.

In a world instrumented with smart sensors and digital platforms, some of our most intimate and information-rich data are being collected and curated by private companies. The opportunities and risks derived from potential knowledge carried within these data streams are undeniable, and the clustering of data within the private sector is challenging traditional data infrastructures and sites of research. The role of private industry in research and development (R&D) has traditionally been limited—especially for earlier stage research—given the high risk, long time horizons, and uncertain returns on investment. However, the information economy has changed the way Silicon Valley and other technology firms operate their business models, which has vast implications for how they respectively innovate. Information drives competitive advantage, and builds upon the emergence of technical infrastructure for collecting, storing, and analyzing data at scale.

Basic research and fundamental inquiry are becoming important innovation priorities for private firms as they tailor algorithms and customize services, and these changes have vast implications for individual privacy and research ethics. This information-intensive innovation does not simply introduce a new source of inquiry, but a shift in the possibilities and boundaries that enable market edge.

This shift challenges prior models of innovation and reconsiders the role of the private firm within the research ecosystem—specifically in regards to Vannevar Bush’s Linear Model of Innovation and Donald Stokes’ Quadrant Model of Scientific Research. This change builds upon prior Silicon Valley innovation models outlined by AnnaLee Saxenian and Henry Chesbrough, but features additional key changes within industry R&D that are fundamentally reshaping the role of the firm within the broader ecosystem. No longer can industry be cast as a place only equipped to grapple exclusively with narrowly applied or developmental research and fully separated or agnostic from users, customers, and citizens. Within this information and data abundant moment, the research and innovation ecosystem is at an inflection point that could alter decades of embedded beliefs and assumptions on who should conduct research and ask fundamental questions, not to mention who should govern and grant access to research data.

This dissertation studies how the rise of data science infrastructure is changing the role of the private firm in the R&D ecosystem. This research works to understand how and under what conditions private sector firms are synthesizing user data (e.g., those picked up by sensors) internally and/or shared externally for research purposes. This dissertation specifically looks at applications of biosensed data for the purposes of social, behavioral, health, or public health research applications. Qualitative and mixed methods are used to research, document, and examine practices within the lens of existing research and innovation theoretical models. Historical frameworks are used to ground and place contemporary practices within broader context.

This research presents three illustrative cases on firms that exemplify different aspects of strategies to adapt to the competitive pressures of information-intensive innovation. The firms include the Lioness smart vibrator, Kinsa smart thermometer, and Basis smart watch. This research establishes findings about how firms are working within the data and R&D landscape, and how new pressures are influencing emerging practices and strategies. Findings outline the changing definitional boundaries of research within the private firm, and evolving practices relating to knowledge sharing and research activities within the firms. This analysis also points to two key emerging challenges firms are coping with, including how to grapple with research ethics and the rise of secrecy practices that may impede collaboration and research strategies implicit with information-intensive innovation.

Research is occurring at many levels within firms, breaking free of any traditional laboratory structure. Collaborations and data sharing with academics for mutually beneficial research partnerships are taking new, largely unstructured forms to meet rising demand and interest. There is fresh demand for new kinds of collaboration models derived from data sharing needs, and exploration into ways of leveraging research practices and incorporating academic research curiosity across firms.

This dissertation concludes by summarizing the importance of reconsidering the role of the firm within the broader R&D ecosystem and broader policy considerations. Programs to help structure and incentivize private/academic research collaborations should be considered, and private firms should evaluate their internal protocols and strategies in light of this changing landscape.  

The creators of technical infrastructure are under social and legal pressure to comply with expectations that can be difficult to translate into computational and business logics. This dissertation bridges this gap through three projects that focus on privacy engineering, information security, and data economics, respectively. These projects culminate in a new formal method for evaluating the strategic and tactical value of data: data games. This method relies on a core theoretical contribution building on the work of Shannon, Dretske, Pearl, Koller, and Nissenbaum: a definition of situated information flow as causal flow in the context of other causal relations and strategic choices.

 The first project studies privacy engineering's use of Contextual Integrity theory (CI), which defines privacy as appropriate information flow according to norms specific to social contexts or spheres. Computer scientists using CI have innovated as they have implemented the theory and blended it with other traditions, such as context-aware computing. This survey examines computer science literature using Contextual Integrity and discovers, among other results, that technical and social platforms that span social contexts challenge CI's current commitment to normative social spheres. Sociotechnical situations can and do defy social expectations with cross-context clashes, and privacy engineering needs its normative theories to acknowledge and address this fact.  This concern inspires the second project, which addresses the problem of building computational systems that comply with data flow and security restrictions such as those required by law. Many privacy and data protection policies stipulate restrictions on the flow of information based on that information's original source. We formalize this concept of privacy as Origin Privacy. This formalization shows how information flow security can be represented using causal modeling. Causal modeling of information security leads to general theorems about the limits of privacy by design as well as a shared language for representing specific privacy concepts such as noninterference, differential privacy, and authorized disclosure.

 The third project uses the causal modeling of information flow to address gaps in current theory of data economics. Like CI, privacy economics has focused on individual economic contexts and so has been unable to comprehend an information economy that relies on the flow of information across contexts. Data games, an adaptation of Multi-Agent Influence Diagrams for mechanism design, are used to model the well known economic contexts of principal-agent contracts and price differentiation as well as new contexts such as personalized expert services and data reuse. This work reveals that information flows are not goods but rather strategic resources, and that trade in information therefore involves market externalities.