Abstracting Fairness: Oracles, Metrics, and Interpretability

It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of ``true'' fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth; moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every ``truly fair'' classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness. Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracle's conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair. Our results have implications for interpretablity -- a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be ``unfair'' or illegitimately derived.Comment: 17 pages, 1 figur

Detection and fine-grained classification of cyberbullying events

In the current era of online interactions, both positive and negative experiences are abundant on the Web. As in real life, negative experiences can have a serious impact on youngsters. Recent studies have reported cybervictimization rates among teenagers that vary between 20% and 40%. In this paper, we focus on cyberbullying as a particular form of cybervictimization and explore its automatic detection and fine-grained classification. Data containing cyberbullying was collected from the social networking site Ask.fm. We developed and applied a new scheme for cyberbullying annotation, which describes the presence and severity of cyberbullying, a post author's role (harasser, victim or bystander) and a number of fine-grained categories related to cyberbullying, such as insults and threats. We present experimental results on the automatic detection of cyberbullying and explore the feasibility of detecting the more fine-grained cyberbullying categories in online posts. For the first task, an F-score of 55.39% is obtained. We observe that the detection of the fine-grained categories (e.g. threats) is more challenging, presumably due to data sparsity, and because they are often expressed in a subtle and implicit way

Managing Information Technology for Strategic Flexibility and Agility: Rethinking Conceptual Models, Architecture, Development, and Governance

The concepts of strategic flexibility and strategic agility have received much attention recently as businesses face increasingly uncertainandcompetitivemarkets(Hittetal.1998;Sanchez1997). However,formanyfirms,existingITassetsandcapabilities pose a serious impediment to strategic agility. Some firms that have successfully implemented enterprise systems are now finding that these systems can be inflexible and difficult to change. Other recent work illustrates that specific choices about IT can enable or constrain a firm’s strategic abilities to respond to changes in the competitive marketplace (Sambamurthy 2000; Weill et al. 2002). If indeed strategic flexibility and agility have become critical imperatives for businesses, then a critical question for IS researchers and practitioners is what can be done to better position IT to enable strategic agility? This panel session proposes to examine the implications of the strategic agility and flexibility imperatives for the IS discipline from four critical perspectives: conceptual models of IS and strategic enablement, IT architecture, IS development, and IT governance. Our rationale is that these comprise four key domains that impact IT use within business organizations, specifically how we think about the role of IT in business, how we design and manage core IT infrastructure and architecture, how new IS applications are developed and implemented, and the allocation of roles and responsibilities for managing IT resources and capabilities. The panelists will argue that significant rethinking and new insights are required to guide IS practice in satisfying the demands for business flexibility and agility, and that future research is needed to identify ways in which IT can be managed to provide these outcomes

Spooky Interaction and its Discontents: Compilers for Succinct Two-Message Argument Systems

We are interested in constructing short two-message arguments for various languages, where the complexity of the verifier is small (e.g. linear in the input size, or even sublinear if it is coded properly). Suppose that we have a low communication public-coin interactive protocol for proving (or arguing) membership in the language. We consider a ``compiler\u27\u27 from the literature that takes a protocol consisting of several rounds and produces a two-message argument system. The compiler is based on any Fully Homomorphic Encryption (FHE) scheme, or on PIR (under additional conditions on the protocol). This compiler has been used successfully in several proposed protocols. We investigate the question of whether this compiler can be proven to work under standard cryptographic assumptions. We prove: (i) If FHEs or PIR systems exist, then there is a sound interactive proof protocol that, when run through the compiler, results in a protocol that is not sound. (ii) If the verifier in the original protocol runs in logarithmic space and has no ``long-term\u27\u27 secret memory (a generalization of public coins), then the compiled protocol is sound. This yields a succinct two-message argument system for any language in NC, where the verifier\u27s work is linear (or even polylog if the input is coded appropriately). This is the first (non trivial) two-message succinct argument system that is based on a standard polynomial-time hardness assumption

Evaluating the Patient-Reported Outcomes Measurement Information System scales in acute intermittent porphyria.

PurposeAcute intermittent porphyria (AIP) is a rare inborn error of heme biosynthesis characterized by life-threatening acute attacks. Few studies have assessed quality of life (QoL) in AIP and those that have had small sample sizes and used tools that may not have captured important domains.MethodsBaseline data from the Porphyrias Consortium's Longitudinal Study were obtained for 259 patients, including detailed disease and medical history data, and the following Patient-Reported Outcomes Measurement Information System (PROMIS) scales: anxiety, depression, pain interference, fatigue, sleep disturbance, physical function, and satisfaction with social roles. Relationships between PROMIS scores and clinical and biochemical AIP features were explored.ResultsPROMIS scores were significantly worse than the general population across all domains, except depression. Each domain discriminated well between asymptomatic and symptomatic patients with symptomatic patients having worse scores. Many important clinical variables like symptom frequency were significantly associated with domain scores in univariate analyses, showing responsiveness of the scales, specifically pain interference and fatigue. However, most regression models only explained ~20% of the variability observed in domain scores.ConclusionPain interference and fatigue were the most responsive scales in measuring QoL in this AIP cohort. Future studies should assess whether these scales capture longitudinal disease progression and treatment response

Automatic detection and prevention of cyberbullying

The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories