Candidates’ attitudes towards various personnel selection methods get attention of organizational and work psychology specialists because of various reasons. The most important reason is that individuals’ attitudes towards personnel selection methods influence their latter behavior. Although there is a substantial amount of studies carried out in different countries, there is no data from Lithuanian and Swedish samples. The aim of current study was to analyze the attitudes towards personnel selection methods among Lithuanian students, Lithuanian employees and Swedish students. The participants (197 students and 86 employees) filled in a questionnaire, which includes short descriptions of 10 personnel selection methods as well as items about fairness of these methods. According to the results of the study, work-sample tests were ranked as the fairest personnel selection method in the Lithuanian sample. The fairest personnel selection methods in Swedish sample were work-sample tests, interview, resumes, and personal references. Lithuanian students ranked the fairness of written ability test and honesty test more favorably than Swedish students, but Swedish students tended to rank as more favorable interview, resumes, personal references and personal contacts. Personal contacts and graphology were ranked the lowest on fairness dimension in Lithuanian sample, and Swedes ranked only graphology as the least fair personnel selection method. Lithuanian employees ranked personal references, personal contacts and graphology more favorably than Lithuanian students. In Lithuanian students sample, perception of personnel selection method as a scientifically proved, logic and precise or providing an opportunity to show one’s skills, had the strongest connection with favorability ranking of personnel selection method’s fairness. In the Lithuanian employees and the Swedish students samples, perception of the method as logic or providing an opportunity to show one’s skills, had the strongest link with fairness.

<p>Candidates’ attitudes towards various personnel selection methods get attention of organizational and work psychology specialists because of various reasons. The most important reason is that individuals’ attitudes towards personnel selection methods influence their latter behavior. Although there is a substantial amount of studies carried out in different countries, there is no data from Lithuanian and Swedish samples. The aim of current study was to analyze the attitudes towards personnel selection methods among Lithuanian students, Lithuanian employees and Swedish students. The participants (197 students and 86 employees) filled in a questionnaire, which includes short descriptions of 10 personnel selection methods as well as items about fairness of these methods. According to the results of the study, work-sample tests were ranked as the fairest personnel selection method in the Lithuanian sample. The fairest personnel selection methods in Swedish sample were work-sample tests, interview, resumes, and personal references. Lithuanian students ranked the fairness of written ability test and honesty test more favorably than Swedish students, but Swedish students tended to rank as more favorable interview, resumes, personal references and personal contacts. Personal contacts and graphology were ranked the lowest on fairness dimension in Lithuanian sample, and Swedes ranked only graphology as the least fair personnel selection method. Lithuanian employees ranked personal references, personal contacts and graphology more favorably than Lithuanian students. In Lithuanian students sample, perception of personnel selection method as a scientifically proved, logic and precise or providing an opportunity to show one’s skills, had the strongest connection with favorability ranking of personnel selection method’s fairness. In the Lithuanian employees and the Swedish students samples, perception of the method as logic or providing an opportunity to show one’s skills, had the strongest link with fairness.</p>

Kandidatų požiūriai į įvairius atrankos metodus traukia organizacinės ir darbo psichologijos atstovų dėmesį dėl įvairių priežasčių, viena svarbiausių jų – tai, jog individų požiūriai į personalo atrankos metodus įtakoja jų vėlesnį elgesį. Ir nors tyrimų atlikta skirtingose šalyse nemažai, tačiau jokių duomenų nerandama Lietuvos ir Švedijos imtims. Šiame darbe iškeliamas tikslas - ištirti lietuvių studentų, lietuvių darbuotojų ir švedų studentų požiūrį į personalo atrankos metodus. Tiriamieji (197 studentai ir 86 darbuotojai) pildė Steiner ir Gilliland (1996) klausimyną, kurį sudaro trumpi 10 atrankos metodų aprašymai ir teiginiai apie šių metodų teisingumą. Rezultatai parodė, jog darbo pavyzdžio testai vertinami kaip teisingiausias personalo atrankos metodas lietuvių imtyje. Švedų imtyje teisingiausi personalo atrankos metodai yra šie: darbo pavyzdžio testai, interviu, gyvenimo aprašymas, asmeninės rekomendacijos. Lietuviai studentai palankiau vertina gebėjimų testų, sąžiningumo testų teisingumą nei švedai studentai, tačiau švedai studentai palankiau nei lietuviai studentai vertina interviu, gyvenimo aprašymą, asmenines rekomendacijas ir asmenines pažintis. Prasčiausiai teisingumo atžvilgiu lietuviai vertina asmenines pažintis ir grafologiją, o švedų grupėje vien tik grafologija vertinama kaip mažiausiai teisinga personalo atrankos procedūra. Lietuviai darbuotojai asmeninių rekomendacijų, asmeninių pažinčių ir grafologijos teisingumą vertina palankiau nei lietuviai... [toliau žr. visą tekstą] / Candidates’ attitudes towards various personnel selection methods get attention of organizational and work psychology specialists because of various reasons. The most important reason is that individuals’ attitudes towards personnel selection methods influence their latter behavior. Although there is a substantial amount of studies carried out in different countries, yet there is no data for Lithuanian and Swedish samples. The aim of current study is to analyze the attitudes towards personnel selection methods among Lithuanian students, Lithuanian employees and Swedish students. Participants of the study (197 students and 86 employees) filled in the questionnaire, which includes short descriptions of 10 personnel selection methods as well as items about fairness of these methods. According to the results of the study, work-sample tests are ranked as the fairest personnel selection method in Lithuanian sample. The fairest personnel selection methods in Swedish sample are work-sample tests, interview, resumes, and personal references. Lithuanian students rank the fairness of written ability test and honesty test more favorably than Swedish students, but Swedish students tend to rank more favorably interview, resumes, personal references and personal contacts. Personal contacts and graphology were ranked the lowest on fairness dimension in Lithuanian sample, and Swedes ranked only graphology as the least fair personnel selection method. Lithuanian employees rank personal... [to full text]

Text in Afrikaans / Werknemers is benewens sekere hoogs uitsonderlike gevalle altyd voor ontslag op substantiewe - en prosessuele billikheid geregtig, hetsy in 'n individuele ofkollektiewe verband. Prosessuele billikheid in besonder het 'n inherente waarde, o.a. omdat die uiteinde van 'n proses nie voorspel kan word nie. Die werkgewer word ook daardeur in staat gestel om die feite te bekom, en arbeidsvrede word daardeur gehandhaaf. Van verdere belang vir prosessuele billikheid is die nakoming van eie of ooreengekome prosedures, die beskikbaarstelling van genoegsame inligting, voorafkennisgewing en bona fide optrede deur die werkgewer. Die primere remedie in die geval van 'n onbillike ontslag is herindiensstelling, alhoewel herindiensstelling nie in die geval van 'n prosessuele onbillike ontslag beveel behoort te word nie. Die riglyne soos in die verlede deur die howe en arbiters ontwikkel is grootliks in die Konsepwet op Arbeids= verhoudinge, soos bevestig in die Wet op Arbeidsverhoudinge, 1995, gekodifiseer. / Prior to dismissal employees are always entitled to substantive - and procedural fairness, be it in an individual or a collective context, subject to highly exceptional circumstances. Procedural fairness in particular has an inherent value, inter alia because the outcome of a process cannot be predicted. The employer also thereby establishes the facts and by conducting a process, labour peace is promoted. Also of importance for procedural fairness is adherance to own or agreed procedures, providing the employee with sufficient information, prior notification and bona fide conduct by the employer. The primary remedy in the case of an unfair dismissal is reinstatement, though reinstatement should not follow in the case of a dismissal which is (only) procedurally unfair. The guidelines as developed by the courts and arbitrators have largely been codified in the Draft Labour Relations Bill, as subsequently confirmed in the Labour Relations Act, 1995. / Mercentile Law / LL. M.

This thesis explored the relationship between work overload and procedural justice on job performance. It used planned comparisons to test three hypotheses, which were tested by having the participants (N=132) randomly assigned to groups and perform a proofreading task in two timed intervals. The study design used quantitative methodologies and the procedures and measures were piloted before data collection. A participant exit survey was also employed. Suggestions for future research and study are discussed. The proofreading samples, the exit survey questions and the Mini-Marker Personality Inventory, the informed consent form, and results tables are included.

This study explored the development of the Federal Communications Commission's Fairness Doctrine policy from its beginnings in the 1920's until the FCC eliminated most of its requirements in 1987. The chapters discuss the Reverend Carl D. McIntire's battle with the FCC concerning the policy's impact on free speech in broadcasting. McIntire lost his battle with the FCC and became the first broadcaster to lose his license for Fairness Doctrine violations. The problem in this study focused on the difficulty of reconciling government regulation of broadcasting with the rights of licensees to speak freely and be heard by their listeners. The study concluded that today the FCC advocates First Amendment protection for broadcasters but it remains questionable whether present policy will continue.

Der Aufstieg des maschinellen Lernens (ML) und die rasante Digitalisierung der Wirtschaft haben die Entscheidungsprozesse in der Finanzbranche erheblich verändert. Finanzinstitute setzen zunehmend auf ML, um die Entscheidungsfindung zu unterstützen. Kreditscoring ist eine der wichtigsten ML-Anwendungen im Finanzbereich. Die Aufgabe von Kreditscoring ist die Unterscheidung ob ein Antragsteller einen Kredit zurückzahlen wird. Finanzinstitute verwenden ML, um Scorecards zu entwickeln, die die Ausfallwahrscheinlichkeit eines Kreditnehmers einschätzen und Genehmigungsentscheidungen automatisieren. Diese Dissertation konzentriert sich auf drei große Herausforderungen, die mit dem Aufbau von ML-basierten Scorekarten für die Bewertung von Verbraucherkrediten verbunden sind: (i) Optimierung von Datenerfassungs- und -speicherkosten bei hochdimensionalen Daten von Kreditantragstellern; (ii) Bewältigung der negativen Auswirkungen von Stichprobenverzerrungen auf das Training und die Bewertung von Scorekarten; (iii) Messung und Sicherstellung der Fairness von Instrumenten bei gleichzeitig hoher Rentabilität. Die Arbeit bietet und testet eine Reihe von Instrumenten, um jede dieser Herausforderungen zu lösen und die Entscheidungsfindung in Finanzinstituten zu verbessern. Erstens entwickeln wir Strategien zur Auswahl von Merkmalen, die mehrere unternehmensbezogene Zielfunktionen optimieren. Unsere Vorschläge reduzieren die Kosten der Datenerfassung und verbessern die Rentabilität der Modelle. Zweitens schlagen wir Methoden zur Abschwächung der negativen Auswirkungen von Stichprobenverzerrungen vor. Unsere Vorschläge gleichen die Verluste aufgrund von Verzerrungen teilweise aus und liefern zuverlässigere Schätzungen der künftigen Scorecard-Leistung. Drittens untersucht die Arbeit faire ML-Praktiken in Kreditscoring. Wir katalogisieren geeignete algorithmische Optionen für die Einbeziehung von Fairness-Zielen und verdeutlichen den Kompromiss zwischen Gewinn und Fairness. / The rise of machine learning (ML) and the rapid digitization of the economy has substantially changed decision processes in the financial industry. Financial institutions increasingly rely on ML to support decision-making. Credit scoring is one of the prominent ML applications in finance. The task of credit scoring is to distinguish between applicants who will pay back the loan or default. Financial institutions use ML to develop scoring models to estimate a borrower's probability of default and automate approval decisions. This dissertation focuses on three major challenges associated with building ML-based scorecards in consumer credit scoring: (i) optimizing data acquisition and storage costs when dealing with high-dimensional data of loan applicants; (ii) addressing the adverse effects of sampling bias on training and evaluation of scoring models; (iii) measuring and ensuring the scorecard fairness while maintaining high profitability. The thesis offers a set of tools to remedy each of these challenges and improve decision-making practices in financial institutions. First, we develop feature selection strategies that optimize multiple business-inspired objectives. Our propositions reduce data acquisition costs and improve model profitability and interpretability. Second, the thesis illustrates the adverse effects of sampling bias on model training and evaluation and suggests novel bias correction frameworks. The proposed methods partly recover the loss due to bias, provide more reliable estimates of the future scorecard performance and increase the resulting model profitability. Third, the thesis investigates fair ML practices in consumer credit scoring. We catalog algorithmic options for incorporating fairness goals in the model development pipeline and perform empirical experiments to clarify the profit-fairness trade-off in lending decisions and identify suitable options to implement fair credit scoring and measure the scorecard fairness.

Künstliche Intelligenz und selbst-lernende Systeme, die ihr Verhalten aufgrund vergangener Entscheidungen und historischer Daten adaptieren, spielen eine im- mer größer werdende Rollen in unserem Alltag. Wir sind umgeben von einer großen Zahl algorithmischer Entscheidungshilfen, sowie einer stetig wachsenden Zahl algorithmischer Entscheidungssysteme. Rankings und sortierte Listen von Suchergebnissen stellen dabei das wesentliche Instrument unserer Onlinesuche nach Inhalten, Produkten, Freizeitaktivitäten und relevanten Personen dar. Aus diesem Grund bestimmt die Reihenfolge der Suchergebnisse nicht nur die Zufriedenheit der Suchenden, sondern auch die Chancen der Sortierten auf Bildung, ökonomischen und sogar sozialen Erfolg. Wissenschaft und Politik sorgen sich aus diesem Grund mehr und mehr um systematische Diskriminierung und Bias durch selbst-lernende Systeme. Um der Diskriminierung im Kontext von Rankings und sortierten Suchergeb- nissen Herr zu werden, sind folgende drei Probleme zu addressieren: Zunächst müssen wir die ethischen Eigenschaften und moralischen Ziele verschiedener Sit- uationen erarbeiten, in denen Rankings eingesetzt werden. Diese sollen mit den ethischen Werten der Algorithmen übereinstimmen, die zur Vermeidung von diskri- minierenden Rankings Anwendung finden. Zweitens ist es notwendig, ethische Wertesysteme in Mathematik und Algorithmen zu übersetzen, um sämtliche moralis- chen Ziele bedienen zu können. Drittens sollten diese Methoden einem breiten Publikum zugänglich sein, das sowohl Programmierer:innen, als auch Jurist:innen und Politiker:innen umfasst. / Artificial intelligence and adaptive systems, that learn patterns from past behavior and historic data, play an increasing role in our day-to-day lives. We are surrounded by a vast amount of algorithmic decision aids, and more and more by algorithmic decision making systems, too. As a subcategory, ranked search results have become the main mechanism, by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, educational placement, and even social success of those being ranked. Therefore researchers have become increasingly concerned with systematic biases and discrimination in data-driven ranking models. To address the problem of discrimination and fairness in the context of rank- ings, three main problems have to be solved: First, we have to understand the philosophical properties of different ranking situations and all important fairness definitions to be able to decide which method would be the most appropriate for a given context. Second, we have to make sure that, for any fairness requirement in a ranking context, a formal definition that meets such requirements exists. More concretely, if a ranking context, for example, requires group fairness to be met, we need an actual definition for group fairness in rankings in the first place. Third, the methods together with their underlying fairness concepts and properties need to be available to a wide range of audiences, from programmers, to policy makers and politicians.

<p dir="ltr">Machine learning (ML) algorithms are increasingly being used in a variety of applications and are heavily relied upon to make decisions that impact people’s lives. ML models are often praised for their precision, yet they can discriminate against certain groups due to biased data. These biases, rooted in historical inequities, pose significant challenges in developing fair and unbiased models. Central to addressing this issue is the mitigation of biases inherent in the training data, as their presence can yield unfair and unjust outcomes when models are deployed in real-world scenarios. This study investigates the efficacy of data acquisition, i.e., one of the stages of data preparation, akin to the pre-processing bias mitigation technique. Through experimental evaluation, we showcase the effectiveness of data acquisition, where the data is acquired using data valuation techniques to enhance the fairness of machine learning models.</p>

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don Gruenbacher / Caterina Scoglio / In this dissertation, we investigate research gaps still existing in P2P file-sharing systems: the necessity of fairness maintenance during the content information publishing/retrieving process, and the stranger policies on P2P fairness. First, through a wide range of measurements in the KAD network, we present the impact of a poorly designed incentive fairness policy on the performance of looking up content information. The KAD network, designed to help peers publish and retrieve sharing information, adopts a distributed hash table (DHT) technology and combines itself into the aMule/eMule P2P file-sharing network. We develop a distributed measurement framework that employs multiple test nodes running on the PlanetLab testbed. During the measurements, the routing tables of around 20,000 peers are crawled and analyzed. More than 3,000,000 pieces of source location information from the publishing tables of multiple peers are retrieved and contacted. Based on these measurements, we show that the routing table is well maintained, while the maintenance policy for the source-location-information publishing table is not well designed. Both the current maintenance schedule for the publishing table and the poor incentive policy on publishing peers eventually result in the low availability of the publishing table, which accordingly cause low lookup performance of the KAD network. Moreover, we propose three possible solutions to address these issues: the self-maintenance scheme with short period renewal interval, the chunk-based publishing/retrieving scheme, and the fairness scheme. Second, using both numerical analyses and agent-based simulations, we evaluate the impact of different stranger policies on system performance and fairness. We explore that the extremely restricting stranger policy brings the best fairness at a cost of performance degradation. The varying tendency of performance and fairness under different stranger policies are not consistent. A trade-off exists between controlling free-riding and maintaining system performance. Thus, P2P designers are required to tackle strangers carefully according to their individual design goals. We also show that BitTorrent prefers to maintain fairness with an extremely restricting stranger policy, while aMule/eMule’s fully rewarding stranger policy promotes free-riders’ benefit.