Benchmarking the Privacy-Preserving People Search

People search is an important topic in information retrieval. Many previous studies on this topic employed social networks to boost search performance by incorporating either local network features (e.g. the common connections between the querying user and candidates in social networks), or global network features (e.g. the PageRank), or both. However, the available social network information can be restricted because of the privacy settings of involved users, which in turn would affect the performance of people search. Therefore, in this paper, we focus on the privacy issues in people search. We propose simulating different privacy settings with a public social network due to the unavailability of privacy-concerned networks. Our study examines the influences of privacy concerns on the local and global network features, and their impacts on the performance of people search. Our results show that: 1) the privacy concerns of different people in the networks have different influences. People with higher association (i.e. higher degree in a network) have much greater impacts on the performance of people search; 2) local network features are more sensitive to the privacy concerns, especially when such concerns come from high association peoples in the network who are also related to the querying user. As the first study on this topic, we hope to generate further discussions on these issues.Comment: 4 pages, 5 figure

Multivariate Fairness for Paper Selection

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia

Multivariate Fairness for Paper Selection

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia

Promoting Diversity in Academic Research Communities Through Multivariate Expert Recommendation

Expert recommendation is the process of identifying individuals who have the appropriate knowledge and skills to achieve a specific task. It has been widely used in the educational environment mainly in the hiring process, paper-reviewer assignment, and assembling conference program committees. In this research, we highlight the problem of diversity and fair representation of underrepresented groups in expertise recommendation, factors that current expertise recommendation systems rarely consider. We introduce a novel way to model experts in academia by considering demographic attributes in addition to skills. We use the h-index score to quantify skills for a researcher and we identify five demographic features with which to represent a researcher\u27s demographic profile. We highlight the importance of these features and their role in bias within the academic environment. We utilize these demographic features within an expert recommender system in academia to achieve demographic diversity and increase the exposure of the underrepresented groups using two approaches. In the first approach, we present three different algorithms for scholar recommendation: expertise-based, diversity-based, and a hybrid algorithm that uses a tuning parameter to calibrate the balance between expertise loss and diversity gain. To evaluate the ranking produced by these algorithms, we introduce a modified normalized Discounted Cumulative Gain (nDCG) version that supports multi-dimensional features, and we report diversity gain from each method. Our results show that we can achieve the best possible balance between diversity gain and expertise loss when the tuning parameter value is set around 0.4, giving nearly equal weight to both expertise and diversity. Finally, we explore diversity from the lens of the demographic parity and develop two algorithms to achieve a representative group that reflects the demographics of the recommendation pool. One is inspired by Hill Climbing, a mathematical optimization technique, wherein a solution is built gradually to the problem, and the other one is inspired by the problem of seat allocation in electoral voting systems. We evaluated these algorithms by comparing them to the hybrid algorithm from the previous approach. Our evaluation shows that both approaches provide a better diversity gain as compared to the hybrid algorithm. However, Hill Climbing Diversity is more effective when it comes to expertise savings with a statistically significant result, making it the preferred algorithm to achieve the goal of promoting diversity while maintaining expertise in an expert recommendation process

Answering the Calls of "What's Next" and "Library Workers Cannot Live by Love Alone" through Certification and Salary Research

Members and staff of the American Library Association (ALA) worked diligently over more than a decade to develop a certification program for public library managers. Spurred by a long-standing trend in many other terminal-degree professions that have post-degree, voluntary certifications, the Certified Public Library Administrator Program was born. Legal authority recommended the establishment of a service organization, a 501(c)(6) to manage the program, which has become one of several programs that will be offered to library employees under the imprimatur of ALA. After the American Library Association???Allied Professional Association (ALA-APA) was instituted, advocacy for salary improvement initiatives was appended to the mission. One means of salary advocacy was to improve available data by expanding the scope and usefulness of the ALA Survey of Librarian Salaries, which resulted in the ALA-APA Salary Survey: Non-MLS???Public and Academic, conducted in 2006 and 2007 to collect salary data from more than sixty positions in the field that do not require a master's degree in Library Science. The experience of establishing two certification programs, the Certified Public Library Administrator Program (CPLA??) and the Library Support Staff Certification Program, has been a study in creating new national models of professional development. This article will also discuss the insights that have emerged from fulfilling elements of ALA strategic plans concerning the needs of support staff through certification and the salary survey.published or submitted for publicatio

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