Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Investigating Trade-offs For Fair Machine Learning Systems

Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory manner, with respect to protected attributes such as gender, race, or age. Ensuring fairness is a crucial non-functional property of data-driven Machine Learning systems. Several approaches (i.e., bias mitigation methods) have been proposed in the literature to reduce bias of Machine Learning systems. However, this often comes hand in hand with performance deterioration. Therefore, this thesis addresses trade-offs that practitioners face when debiasing Machine Learning systems. At first, we perform a literature review to investigate the current state of the art for debiasing Machine Learning systems. This includes an overview of existing debiasing techniques and how they are evaluated (e.g., how is bias measured). As a second contribution, we propose a benchmarking approach that allows for an evaluation and comparison of bias mitigation methods and their trade-offs (i.e., how much performance is sacrificed for improving fairness). Afterwards, we propose a debiasing method ourselves, which modifies already trained Machine Learning models, with the goal to improve both, their fairness and accuracy. Moreover, this thesis addresses the challenge of how to deal with fairness with regards to age. This question is answered with an empirical evaluation on real-world datasets

Asteroid Families: properties, recent advances and future opportunities

Collisions are one of the key processes shaping planetary systems. Asteroid families are outcomes of such collisions still identifiable across our solar system. The families provide a unique view of catastrophic disruption phenomena and have been in the focus of planetary scientists for more than a century. Most of them are located in the main belt, a ring of asteroids between Mars and Jupiter. Here we review the basic properties of the families, discuss some recent advances, and anticipate future challenges. This review pays more attention to dynamic aspects such as family identification, age determination, and long-term evolution. The text, however, goes beyond that. Especially, we cover the details of young families that see the major advances in the last years, and we anticipate it will develop even faster in the future. We also discuss the relevance of asteroid families for water-ice content in the asteroid belt and our current knowledge on links between families and main-belt comets.Comment: Review paper to appear in CeMDA's topical collection on "Main Belt Dynamics

Perceptual quality assessment of real-world images and videos

The development of online social-media venues and rapid advances in technology by camera and mobile device manufacturers have led to the creation and consumption of a seemingly limitless supply of visual content. However, a vast majority of these digital images and videos are often afflicted with annoying artifacts during acquisition, subsequent storage, and transmission over the network. All these factors impact the quality of the visual media as perceived by a human observer, thereby compromising their quality of experience (QoE). This dissertation focuses on constructing datasets that are representative of real-world image and video distortions as well as on designing algorithms that accurately predict the perceptual quality of images and videos. The primary goal of this research is to design and demonstrate automatic image and continuous-time video quality predictors that can effectively tackle the widely diverse authentic spatial, temporal, and network-induced distortions -- contrary to all present-day algorithms that operate on single, synthetic visual distortions and predict a single overall quality score for a given video. I introduce an image quality database which contains a large number of images captured using a representative variety of modern mobile devices and afflicted with a widely diverse authentic image distortions. I will also describe the design of an online crowdsourcing system which aided a very large-scale image quality assessment subjective study. This data collection facilitated the design of a new image quality predictor that is founded on the principles of natural scene statistics of images in different color spaces and transform domains. This new quality method is capable of assessing the quality of images with complex mixtures of distortions and yields high correlation with human perception. Pertaining to videos, this dissertation describes a video quality database created to understand the impact of network-induced distortions on an end user's quality of experience. I present the details of a large-scale subjective study that I conducted to gather continuous-time ground truth QoE scores on a collection of 180 videos afflicted with diverse stalling events. I also present my analysis of the temporal variations in the perceived QoE due to the time-varying video quality and present insights on the impact of relevant human cognitive aspects such as long-term and short-term memory and recency on quality perception. Next, I present a continuous-time objective QoE predicting model that effectively captures the complex interactions between the aforementioned human cognitive elements, spatial and temporal distortions, properties of stalling events, and models the state of any given client-side network buffer. I also show how the proposed framework can be extended by further supplementing with any number of additional inputs (or by eliminating any ineffective ones), based on the information available at the content providers during the design of adaptive stream-switching algorithms. This QoE predictor supports future research in the design of quality-aware stream-switching algorithms which could control the position, location, and length of stalls, given a network bandwidth budget and the end user's device information, such that the end user's QoE is maximized.Computer Science

Large Language Models and Knowledge Graphs: Opportunities and Challenges

Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.Comment: 30 page

Minor planet astrophotometry

Historically, the Minor Planet Center (MPC) has concentrated on improving the quality of the astrometric observations and the resulting orbits of minor planets. In light of long-standing complaints in the literature about the quality of the photometric parameters for the minor planets, there has been a need to improve the quality of the absolute magnitudes, H, and slope parameters, G. However, this task is complex, as the bulk of the minor-planet magnitude estimates are suppl ied by the astrometric observers. These observations are not made through standard fillers and are made with respect to the (indifferent) magnitudes in numerous astrometric reference catalogues. Such magnitude estimates are labelled "astrophotometry", to reflect their low quality. This thesis describes a method for correcting the catalogue- and observer-specific errors present in the astrophotometry. This method was applied to more than 70 million astrometric observations with magnitude estimates. New H determinations have been made for 322 607 numbered minor planets, while new G determinations have been made for 64 348 numbered minor planets. New assumed G values have been determined for 258 259 numbered minor planets. Analysis of the results shows that the problems identified in the literature have been removed, particularly the -0.5 magnitude offset at H≈14 that is present in the current MPC HG data set. Implications of the new H magnitudes on the albedos determined by the WISE space mission and on the differential H distributions of various types of solar-system object are discussed

Fairness in Information Access Systems

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space

Corporate Innovation Activism in a Multidivisional Firm: Rationale, Genesis, Evolution

Various streams of foundational management literatures imply that corporate managers can play a role in the management of intra-organizational innovation processes. However, management scholars have largely assumed that corporate managers do not become actively involved in the management of intra-organizational innovation processes occurring within multidivisional firms. This assumption contrasts with the importance given in the management literature to innovation as an enabler of organizational long-term survival. To address this contrast, my dissertation explores why and how corporate managers adopt an active approach to the management of intra-organizational innovation processes in complex multidivisional firms. In the first paper, I map extant knowledge of innovation mechanisms onto an evolutionary multilevel framework. I synthesize uncovered mechanisms into structural, behavioural, and routinized corporate approaches to innovation management. I conclude this paper by proposing a comprehensive research agenda for exploring complex interactions between top-down and bottom-up innovation processes occurring within a multidivisional firm. In the second paper, I propose a mid-range theory of corporate innovation activism elaborating two novel concepts. The corporate innovation synergy concept encapsulates mechanisms available to corporate managers to increase the efficiency of intra-organizational innovation processes. The corporate innovation value-added concept concerns mechanisms available to corporate managers to qualitatively improve intra-organizational innovation processes in ways unavailable at the business unit level. I organize my arguments into a theoretical model and discuss limitations of my theory, offering important opportunities for future research. In the third paper, I explore the genesis of corporate managers’ capability to influence innovation management in a multidivisional firm; I call this the corporate innovation function. I combine proprietary narrative data with archival records to study the development of the corporate innovation function in 20 large multidivisional firms. Based on my observations of 17 corporate innovation processes, I develop a corporate innovation function typology comprised of collaborative, parallel-capability, and sponsorship corporate innovation function models. I link differences across the corporate innovation function configurations to firm-level innovation performance. In the fourth paper, I elaborate on the concept of dynamic corporate innovation capability, which enables a multidivisional firm to continuously discover, evaluate, and monetize innovations that are novel to the firm and the markets in which the firm operates. Exploiting further the proprietary narrative and archival dataset, I first establish the prototypical role of a senior innovation manager and identify four underlying mechanisms that enable the establishment of a dynamic corporate innovation capability: senior innovation manager legitimacy, corporate innovation ambition, corporate innovation processes, and corporate innovation routines. Using a system dynamics approach, I synthesize my findings in a dynamic model, disentangling the complex process of maintaining exploration in an organizational environment biased towards exploitation