Fairness Testing: Testing Software for Discrimination

This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior. Evidence of software discrimination has been found in modern software systems that recommend criminal sentences, grant access to financial products, and determine who is allowed to participate in promotions. Our approach, Themis, generates efficient test suites to measure discrimination. Given a schema describing valid system inputs, Themis generates discrimination tests automatically and does not require an oracle. We evaluate Themis on 20 software systems, 12 of which come from prior work with explicit focus on avoiding discrimination. We find that (1) Themis is effective at discovering software discrimination, (2) state-of-the-art techniques for removing discrimination from algorithms fail in many situations, at times discriminating against as much as 98% of an input subdomain, (3) Themis optimizations are effective at producing efficient test suites for measuring discrimination, and (4) Themis is more efficient on systems that exhibit more discrimination. We thus demonstrate that fairness testing is a critical aspect of the software development cycle in domains with possible discrimination and provide initial tools for measuring software discrimination.Comment: Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017. Fairness Testing: Testing Software for Discrimination. In Proceedings of 2017 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), Paderborn, Germany, September 4-8, 2017 (ESEC/FSE'17). https://doi.org/10.1145/3106237.3106277, ESEC/FSE, 201

Academic profession, contingent employment and career pathways during a crisis

Society for research into Higher Education (SRHE)

Database Learning: Toward a Database that Becomes Smarter Every Time

In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries. We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM SIGMOD conference 201

VerdictDB: Universalizing Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201

Relational practices and reflexivity: Exploring the responses of women entrepreneurs to changing household dynamics

This qualitative study explores how and why women, positioned as mothers, wives, or carers, navigate changing household dynamics, related to care and reproductive resources, and become entrepreneurial. Drawing on relational reflexivity, we show how women’s embodied, intimate relations with important others in the household form the focal point for entrepreneurial activities and offer evidence of their entrepreneurial agency. Our analysis reveals the emergence of three relational practices that result in a new venture as the entrepreneurial response of women. We critically evaluate normative analyses on gender, entrepreneurship, and household

Women’s experiences of menopause at work and performance management

Presenting findings from our global evidence review of menopause transition and economic participation emboldened us to establish a menopause policy at the university where we all worked at the time. Our report was published in July 2017 and the policy was in place by November that year. Our critical reflection on this activism focuses on issues that are not commonly recognized around such interventions, and which we ourselves have only been able to acknowledge through engaged action. Challenges remain in normalizing menopause in organizations, specifically around gendered ageism and performance management. In drawing on Meyerson and Kolb’s framework for understanding gender in organizations, we highlight how policies are both vital and yet insufficient in and of themselves in revising the dominant discourse around menopause at work. At the same time, we highlight the importance and shortcomings of academic activism within these processes

Being a Self-Employed Older Woman: From Discrimination to Activism

This article presents an autobiographical account of an older woman’s lived experience of self-employment. Little is known about women who experience ongoing self-employment into their 50s and beyond. Shoshanna’s personal narrative describes her experiences and the challenges she has faced as she reflects upon her attempts to grow and sustain her business and the implications of ageism and gender inequality in laying a claim to entrepreneurship. The narrative proceeds to reflect on her activist work, as it is constructed through the creation of a social enterprise to support older people. Shoshanna’s narrative provides valuable insights into the intersection of age and gender in self-employment moving from discrimination to active support