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

Improving User Involvement Through Live Collaborative Creation

Creating an artifact - such as writing a book, developing software, or performing a piece of music - is often limited to those with domain-specific experience or training. As a consequence, effectively involving non-expert end users in such creative processes is challenging. This work explores how computational systems can facilitate collaboration, communication, and participation in the context of involving users in the process of creating artifacts while mitigating the challenges inherent to such processes. In particular, the interactive systems presented in this work support live collaborative creation, in which artifact users collaboratively participate in the artifact creation process with creators in real time. In the systems that I have created, I explored liveness, the extent to which the process of creating artifacts and the state of the artifacts are immediately and continuously perceptible, for applications such as programming, writing, music performance, and UI design. Liveness helps preserve natural expressivity, supports real-time communication, and facilitates participation in the creative process. Live collaboration is beneficial for users and creators alike: making the process of creation visible encourages users to engage in the process and better understand the final artifact. Additionally, creators can receive immediate feedback in a continuous, closed loop with users. Through these interactive systems, non-expert participants help create such artifacts as GUI prototypes, software, and musical performances. This dissertation explores three topics: (1) the challenges inherent to collaborative creation in live settings, and computational tools that address them; (2) methods for reducing the barriers of entry to live collaboration; and (3) approaches to preserving liveness in the creative process, affording creators more expressivity in making artifacts and affording users access to information traditionally only available in real-time processes. In this work, I showed that enabling collaborative, expressive, and live interactions in computational systems allow the broader population to take part in various creative practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145810/1/snaglee_1.pd

#MDXPD Product Design 2020

MDXPD Magazine 2019-20 details the student, staff and collaborative partner research and practice work across Middlesex University Department of Design Engineering and Mathematics and Programmes BA(Hons) Product Design and BEng (Hons) Product Design Engineering

Business model innovation and digital transformation in global management consulting firms

Purpose: This paper contains an exploratory analysis of the business model innovations (BMIs) that management consulting firms (MCFs) undertake to remain competitive during digital transformation. Design/methodology/approach: This paper uses data from a longitudinal multiple case study of the European practices of major global MCFs to provide an overview of how they reconfigure their business model (BM) to gain competitive advantages. It maps BMIs in MCFs through value creation innovation, value proposition innovation and value capturing innovation. Findings: There is a shift in value proposition from solely giving advice or supporting information technology (IT) implementation to providing end-to-end digital solutions. To materialize value propositions, MCFs acquire new knowledge and digital assets through talent scouting, and mergers and acquisitions (M&As). MCFs rely heavily on complementary knowledge and capabilities of actors within ecosystems; thus, they focus on expanding, creating their ecosystems and adopting platforms' configuration and characteristics. Research limitations/implications: Inductively, the authors reached an analytical generalization through six propositions and a theoretical frame that embeds propositions in the previous literature. Future research should test them across the overall management consulting industry. Practical implications: MCFs are recognized as drivers of innovation and BMIs in most client firms. However, MCFs are rarely analyzed with respect to their BMIs. Understanding how MCFs innovate their business models (BMs) to provide digital transformation (DT) consulting services is relevant for delivering management innovation across industries. Originality/value: This is the first exploratory study on BMI inside global MCFs during DT

20-20 listening: a sound documentary dedicated to the study of listening experiences in acoustic environments

The intersection of sound and design is an exciting and complex space for artistic experimentation and research. My approach for this work was to design, write, record and mix seven podcast episodes that narrate my analysis and interpretation of how we listen to sounds and interpret their meaning. Each episode is dedicated to one topic, and presents multiple sound samples that illustrate my take on the subject. The episodes cover the basics of listening, how sound conveys information about objects in environments and how soundscapes are ubiquitous. They include how the music, sounds and noises in film convey meaning, represent physical qualities and produce an emotional connection with viewers. I also introduce an episode on dynamic audio in video games, and the process of design conceptualization and artistic interventions to make simple sound-based prototypes for people to make sounds, play and enjoy. Furthermore, I introduce the story of a whistling language in the Canary Islands to illustrate the concept of “acoustic community,” and how soundmarks create meaning and a sense of belonging in a social group. I also present a sonic composition that uses speech, sounds, noises and music to create an artistic narrative. This project is the sum of experiments in my sonic journey; it is an audio documentary that uses the listener’s focal attention to create stories about listening

Deep Learning for Free-Hand Sketch: A Survey

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.Comment: This paper is accepted by IEEE TPAM