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Negar Rostamzadeh

Negar Rostamzadeh

Negar Rostamzadeh is a Senior Research Scientist at Google Responsible AI, where she studies the social impact of machine learning technologies and evaluation systems.
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Google Publications
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    Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction
    Shalaleh Rismani
    Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), Association for Computing Machinery (2024), pp. 1-17 (to appear)
    Preview abstract Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning (ML) systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through reflexive thematic analysis of workshop data, we highlight theorization of T2I use, harm, and harm-reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model affordances. Theories of harm articulate T2I models as harmed by engineering efforts to eliminate glitches and product policy efforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI efforts. View details
    Creative ML Assemblages: The Interactive Politics of People, Processes, and roducts
    Ramya Malur Srinivasan
    Katharina Burgdorf
    Jennifer Lena
    ACM Conference on Computer Supported Cooperative Work and Social Computing (2024) (to appear)
    Preview abstract Creative ML tools are collaborative systems that afford artistic creativity through their myriad interactive relationships. We propose using ``assemblage thinking" to support analyses of creative ML by approaching it as a system in which the elements of people, organizations, culture, practices, and technology constantly influence each other. We model these interactions as ``coordinating elements" that give rise to the social and political characteristics of a particular creative ML context, and call attention to three dynamic elements of creative ML whose interactions provide unique context for the social impact a particular system as: people, creative processes, and products. As creative assemblages are highly contextual, we present these as analytical concepts that computing researchers can adapt to better understand the functioning of a particular system or phenomena and identify intervention points to foster desired change. This paper contributes to theorizing interactions with AI in the context of art, and how these interactions shape the production of algorithmic art. View details
    Preview abstract Inappropriate design and deployment of machine learning (ML) systems lead to negative downstream social and ethical impacts -- described here as social and ethical risks -- for users, society, and the environment. Despite the growing need to regulate ML systems, current processes for assessing and mitigating risks are disjointed and inconsistent. We interviewed 30 industry practitioners on their current social and ethical risk management practices and collected their first reactions on adapting safety engineering frameworks into their practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide an appropriate structure for social and ethical risk assessment and mitigation processes. However, we also find nontrivial challenges in integrating such frameworks in the fast-paced culture of the ML industry. We call on the CHI community to strengthen existing frameworks and assess their efficacy, ensuring that ML systems are safer for all people. View details
    Identifying Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
    Shalaleh Rismani
    Kathryn Henne
    AJung Moon
    Paul Nicholas
    N'Mah Yilla-Akbari
    Jess Gallegos
    Emilio Garcia
    Gurleen Virk
    Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, Association for Computing Machinery, 723–741
    Preview abstract Understanding the broader landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic and machine learning (ML) technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems arising at the micro-, meso-, and macro-levels of society. We present an applied taxonomy of sociotechnical harms to support more systematic surfacing of potential harms in algorithmic systems. Based on a scoping review of prior research on harms from AI systems (n=172), we identified five major themes related to sociotechnical harms — allocative, quality-of-service, representational, social system, and interpersonal harms. We describe these categories of harm, and present case studies that illustrate the usefulness of the taxonomy. We conclude with a discussion of challenges and under-explored areas of harm in the literature, which present opportunities for future research. View details
    Preview abstract Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks - both of which are not designed explicitly examine social and ethical risks - can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model. View details
    Preview abstract Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems that directly affect people’s lives. Many of the ethical issues surrounding the use of ML in healthcare stem from structural inequalities underlying the way we collect, use, and handle data. Developing guidelines to improve documentation practices regarding the creation, use, and maintenance of ML healthcare datasets is therefore of critical importance. In this work, we introduce Healthsheet, a contextualized adaptation of the original datasheet questionnaire for health-specific applications. Through a series of semi-structured interviews, we adapt the datasheets for healthcare data documentation. As part of the Healthsheet development process and to understand the obstacles researchers face in creating datasheets, we worked with three publicly-available healthcare datasets as our case studies, each with different types of structured data: Electronic health Records (EHR), clinical trial study data, and smartphone-based performance outcome measures. Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly Healthsheets as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns. View details
    Preview abstract Testing, within the machine learning (ML) community, has been predominantly about assessing a learned model's predictive performance measured against a test dataset. This test dataset is often a held-out subset of the dataset used to train the model, and hence expected to follow the same data distribution as the training dataset. While recent work on robustness testing within ML has pointed to the importance of testing against distributional shifts, these efforts also focus on estimating the likelihood of the model making an error against a reference dataset/distribution. In this paper, we argue that this view of testing actively discourages researchers and developers from looking into many other sources of robustness failures, for instance corner cases. We draw parallels with decades of work within software engineering testing focused on assessing a software system against various stress conditions, including corner cases, as opposed to solely focusing on average-case behaviour. Finally, we put forth a set of recommendations to broaden the view of machine learning testing to a rigorous practice. View details
    Preview abstract In order to build trust that a machine learned model is appropriate and responsible within a systems context involving technical and human components, a broad range of factors typically need to be considered. However in practice model evaluations frequently focus on only a narrow range of expected predictive behaviours. This paper examines the critical evaluation gap between the idealized breadth of concerns and the observed narrow focus of actual evaluations. In doing so, we demonstrate which values are centered—and which are marginalized—within the machine learning community. Through an empirical study of machine learning papers from recent high profile conferences, we demonstrate the discipline’s general focus on a small set of evaluation methods. By considering the mathematical formulations of evaluation metrics and the test datasets over which they are calculated, we draw attention to which properties of models are centered in the field. This analysis also reveals an important gap: the properties of models which are frequently neglected or sidelined during evaluation. By studying the structure of this gap, we demonstrate the machine learning discipline’s implicit assumption of a range of commitments which have normative impacts; these include commitments to consequentialism, abstractability from context, the quantifiability of impacts, the irrelevance of non-predictive features, and the equivalence of different failure modes. Shedding light on these assumptions and commitments enables us to question their appropriateness for different ML system contexts, and points the way towards more diverse and contextualized evaluation methodologies which can be used to more robustly examine the trustworthiness of ML models. View details
    Art Sheets for Art Datasets
    Ramya Malur Srinivasan
    Jordan Jennifer Famularo
    Beth Coleman
    NeurIPS Dataset & Benchmark track (2021)
    Preview abstract As machine learning (ML) techniques are being employed to authenticate artworks and estimate their market value, computational tasks have expanded across a variety of creative domains and datasets drawn from the arts. With recent progress in generative modeling, ML techniques are also used for simulating artistic styles and for producing new content in various media such as music, visual arts, poetry, etc. While this progress has opened up new creative avenues, it has also paved the way for adverse downstream effects such as cultural appropriation (e.g., cultural misrepresentation, offense, and undervaluing) and amplification of gender and racial stereotypes, to name a few. Many such concerning issues stem from the training data in ways that diligent evaluation can uncover, prevent, and mitigate. In this paper, we provide a checklist of questions customized for use with art datasets, building on the questionnaire for datasets provided in Datasheets, by guiding assessment of developer motivation together with dataset provenance, composition, collection, pre-processing, cleaning, labeling, use (including data generation/synthesis), distribution, and maintenance. Case studies exemplify the value of our questionnaire. We hope our work aids ML scientists and developers by providing a framework for responsible design, development, and use of art datasets. View details
    Preview abstract Fashion is one of the ways in which we show ourselves to the world. It is a reflection of our personal decisions and one of the ways in which people distinguish and represent themselves. Fashion matters. Fashion is a form of cultural expression and story telling. In this paper, we discuss the history of the se-Shweshwe fabric fashion, the collection of the se-Shweshwe dataset, and applying the sketch to design image generation technique to have affordable fashion-design. Finally we discuss the ethical considerations on creating and employing a fashion dataset. View details
    Deep Cox Mixtures for Survival Regression
    Proceedings of the 6th Machine Learning for Healthcare Conference, PMLR (2021), pp. 674-708
    Preview abstract Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics. View details
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