Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as “racist” or “biased” by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics; 2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation; 3) a discussion of the pertinent technical and social matters; and 4) an outline of the remaining challenges and future work items, both from technological and social points of view.

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus ``in the wild''), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users' motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of ``good'' measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world. Table of Contents: Preface / Acknowledgments / Introduction and Scope / Approaches Based on Self-Report Methods / Approaches Based on Physiological Measurements / Approaches Based on Web Analytics / Beyond Desktop, Single Site, and Single Task / Enhancing the Rigor of User Engagement Methods and Measures / Conclusions and Future Research Directions / Bibliography / Authors' Biographies / Index

Measuring User Engagement

In recent years, there has been an increasing attention in the literature on the possibility of analyzing social media as a useful complement to traditional off-line polls to monitor an electoral campaign. Some scholars claim that by doing so, we can also produce a forecast of the result. Relying on a proper methodology for sentiment analysis remains a crucial issue in this respect. In this work, we apply the supervised method proposed by Hopkins and King to analyze the voting intention of Twitter users in the United States (for the 2012 Presidential election) and Italy (for the two rounds of the centre-left 2012 primaries). This methodology presents two crucial advantages compared to traditionally employed alternatives: a better interpretation of the texts and more reliable aggregate results. Our analysis shows a remarkable ability of Twitter to ânowcastâ as well as to forecast electoral results.

Using Sentiment Analysis to Monitor Electoral Campaigns: Method Matters-Evidence From the United States and Italy

Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed.

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Big Data and AI – A transformational shift for government: So, what next for research?:

This article presents a quasi-experimental study comparing the impact of two technology-related teacher professional development (TTPD) designs, aimed at helping junior high school science and mathematics teachers design online activities using the rapidly growing set of online learning resources available on the Internet. The first TTPD design (tech-only) focused exclusively on enhancing technology knowledge and skills for finding, selecting, and designing classroom activities with online resources, while the second (tech + pbl) coupled technology knowledge with learning to design problem-based learning (PBL) activities for students. Both designs showed large pre-post gains for teacher participants (N = 36) in terms of self-reported knowledge, skills, and technology integration. Significant interaction effects show that teachers in the tech + pbl group had larger gains for self-reported knowledge and externally rated use of PBL. Three generalized estimating equation (GEE) models were fit to study the impact on students’ (N = 1,247) self reported gains in behavior, knowledge, and attitudes. In the resulting models, students of tech + pbl teachers showed significant increases in gain scores for all three outcomes. By contrast, students of tech-only teachers showed improved gains only in attitudes.

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Comparing technology-related teacher professional development designs: a multilevel study of teacher and student impacts

This paper discusses the use of web metrics tools at four digital libraries, the Instructional Architect, the Library of Congress, the National Science Digital Library, and WGBH Teachers' Domain. We describe some of the issues involved in using web metrics to report on ongoing web site performance. We also describe how web metrics can be used for focused data mining, using session length metrics as our example; and here, we recommend that session length metrics, which were developed to track e-commerce, need to be carefully considered when they are applied in non-e-commerce settings, such as digital libraries. We conclude by discussing some of the current limitations and possibilities of using web metrics to analyze and evaluate digital library use and impact.

Using web metrics to analyze digital libraries

The United States optimizes the efficiency of its growing criminal justice system with algorithms however, legal scholars have overlooked how to frame courtroom debates about algorithmic predictions. In State v Loomis, the defense argued that the court’s consideration of risk assessments during sentencing was a violation of due process because the accuracy of the algorithmic prediction could not be verified. The Wisconsin Supreme Court upheld the consideration of predictive risk at sentencing because the assessment was disclosed and the defendant could challenge the prediction by verifying the accuracy of data fed into the algorithm.

Was the court correct about how to argue with an algorithm?

The Loomis court ignored the computational procedures that processed the data within the algorithm. How algorithms calculate data is equally as important as the quality of the data calculated. The arguments in Loomis revealed a need for new forms of reasoning to justify the logic of evidence-based tools. A “data science reasoning” could provide ways to dispute the integrity of predictive algorithms with arguments grounded in how the technology works.

This article’s contribution is a series of arguments that could support due process claims concerning predictive algorithms, specifically the Correctional Offender Management Profiling for Alternative Sanctions (“COMPAS”) risk assessment. As a comprehensive treatment, this article outlines the due process arguments in Loomis, analyzes arguments in an ongoing academic debate about COMPAS, and proposes alternative arguments based on the algorithm’s organizational context.

Risk assessment has dominated one of the first wide-ranging academic debates within the emerging field of data science. ProPublica investigative journalists claimed that the COMPAS algorithm is biased and released their findings as open data sets. The ProPublica data started a prolific and mathematically-specific conversation about risk assessment as well as a broader conversation on the social impact of algorithms. The ProPublica-COMPAS debate repeatedly considered three main themes: mathematical definitions of fairness, explainable interpretation of models, and the importance of population comparison groups.

While the Loomis decision addressed permissible use for a risk assessment at sentencing, a deeper understanding of daily practice within the organization could extend debates about algorithms to questions about procurement, implementation, or training. The criminal justice organization that purchased the risk assessment is in the best position to justify how one individual’s assessment matches the algorithm designed for its administrative needs. People subject to a risk assessment cannot conjecture how the algorithm ranked them without knowing why they were classified within a certain group and what criteria control the rankings. The controversy over risk assessment algorithms hints at whether procedural due process is the cost of automating a criminal justice system that is operating at administrative capacity.

How to Argue with an Algorithm: Lessons from the COMPAS ProPublica Debate

Reliable public sector information serves as a pivotal source for big data. Government postal codes, for instance, have been crucial for predicting demographics. Confidentiality, however, may be at risk when combined with other sources. Public sector information not only describes government activity but contains material, such as campaign finance filings, produced by outside sources. How does information production impact policy concerns if material is reused for big data projects? Information production is analyzed using a framework of five methods of production. The framework considers information that the public sector writes, publishes, manages, produces through research, and compiles through legal mandates. This paper examines the policy implications of using U.S. federal public sector information in big data projects.

Government information policy in the era of big data

Interoperability sets standards for consistency when integrating information from multiple sources. Trends in e-government have encouraged the production of digital information yet it is not clear if the data produced are interoperable. The objective of the project was to evaluate interoperability by building a retrieval tool that could track United States public policy from the legislative to the executive branch using only machine-readable government information. A case study of policy created during the 2008 financial crisis serves as an illustration to investigate the organizational, technical, syntactic, and operational interoperability of digital sources. The methods of citing law varied widely enough between legislation and regulation to impede consistent automated tracking. The flow of federal policy authorization exemplifies remaining socio-technical challenges in achieving the interoperability of machine-readable government data.

The Interoperability of US Federal Government Information: Interoperability

This paper contributes to the ongoing discourse on social media and elections, by evaluating Twitter activity during the 2012 United States presidential campaign. We specifically ask: what is the correlation between Twitter, polls and the popular vote? In response to current debates on methods, we evaluated multiple configurations of Election Day Twitter activity. The Twitter activity in our study included a commercial proprietary sentiment analysis, dictionary-based sentiment analysis, and volume of messages analyzed at multiple time periods. We normalized all data by converting values into binary proportions for the two major candidates. Bivariate statistical analysis using a chi-square test provided comparative analysis. Our findings suggest that Twitter data analyzed with sentiment analysis better reflects the popular vote than the volume of messages. For further analysis, we compared the binary proportions of the positive sentiment data. Having a Mean Absolute Error (MAE) of 3.11% and a 1.7% point-bias, the Twitter activity analyzed with the proprietary sentiment analysis was within the same proximity as public opinion polls that accurately reflected the popular vote.

Anne L. Washington

Papers

Using web metrics to analyze digital libraries

How to Argue with an Algorithm: Lessons from the COMPAS ProPublica Debate

Government information policy in the era of big data

The Interoperability of US Federal Government Information: Interoperability

What Is the Correlation between Twitter, Polls and the Popular Vote in the 2012 Presidential Election? (Correction)