Contributions in Merlin

Search engines serve as information gatekeepers on a multitude of topics that are prone to gender, ethnicity, and race misrepresentations. In this paper, we specifically look at the image search representation of migrant population groups that are often subjected to discrimination and biased representation in mainstream media, increasingly so with the rise of right-wing populist actors in the Western countries. Using multiple (n = 200) virtual agents to simulate human browsing behavior in a controlled environment, we collect image search results related to various terms referring to migrants (e.g., expats, immigrants, and refugees, seven queries in English and German used in total) from the six most popular search engines. Then, with the aid of manual coding, we investigate which features are used to represent these groups and whether the representations are subjected to bias. Our findings indicate that search engines reproduce ethnic and gender biases common for mainstream media representations of different subgroups of migrant population. For instance, migrant representations tend to be highly racialized, and female migrants as well as migrants at work tend to be underrepresented in the results. Our findings highlight the need for further algorithmic impact auditing studies in the context of representation of potentially vulnerable groups in web search results.

Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Data and material: https://doi.org/10.5281/zenodo.5848794

Addressing the demands of our aging society, technologies such as smart home systems enhance older adults' independence and enable them to live longer at home. Yet, older adults tend to avoid, delegate, or defer decisions, and smart home systems are rarely used by individuals over the age of 65. Drawing on research on choice overload and age labels, we investigate across four studies how the presentation of smart home systems can influence the decision-making process of older adults ages 65+ by mitigating choice complexity and, consequently, choice avoidance. We argue that age-framing of complex systems makes the choice options easier to process for older adults than does price-framing, the current market standard. We find that age-framing positively affects the rate of choice, and we identify reduced choice complexity as the underlying process. Our research further demonstrates that family members, as important co-deciders, evaluate decisions made on age-framed alternatives more positively, thus making it easier for older adults to justify their decisions. We thus contribute to research on the decision-making of older adults in general and on age labels in particular, and we identify important practical implications for providers of high investment products for older adults.

In this study, I examine the validity of the small-cap premium (i.e., the size effect) from 2000 to 2021, using the Swiss Performance Index (SPI) to represent the Swiss stock market. I sort SPI stocks in terms of size (market capitalisation) and value (book-to-market ratio). Then, I construct six portfolios based on these categories to analyse the size effect in relation to performance, as well as statistically. I find that the small market capitalisation portfolio and low bookto-market ratio portfolio returns significantly outperform the benchmark (SPI) over the 21-year period, with this effect amplified during the COVID-19 pandemic. However, I find no significant statistical size effect across the six portfolios.

Central bank purchases under QE programs have the unintended consequence of making some collateral securities more scarce in the market. Such scarcity effects can usually be found in the bilateral repo market, where special repo rates are traded, but prolonged and excessive central bank asset purchases coupled with high a demand for safe assets can also cause a decrease in general collateral repo rates, which in turn reflects the scarcity of the whole class of government bonds. In this dissertation, I study scarcity effects of US Treasuries by investigating the Treasury GCF repo rate. Each $1 trillion purchase of Treasuries by the Fed is associated with a 40-46 basis point decline in the Treasury general collateral repo rate. This suggests moderate general Treasury collateral scarcity effects of quantitative easing in the US.

Multi-Asset Barrier Reverse Convertible (MBRC) are one of the most sold structured products in the world, especially in Switzerland where 200 billion CHF of assets are being sold per year. Some structured products can be evaluated with a close-end solution, which does not exist for MBRCs. There exist multiple methods to price MBRC and this thesis will focus on the implementation of the pricing algorithm of MBRC suggested by Lindauer and Seiz (2008). In this thesis also ceteris paribus effects of influencing parameters are measured and described. It is observed that volatility, maturity, type of the product and a barrier level have a big impact on the final price, whereas the dividend yield and risk-free rate are influencing each other in an unknown way.

The role structure of a top management team (TMT)—the roles of TMT members and the relationships among those roles—has important implications for how TMT members work together as a group in directing an organization and shaping its strategy. Although the importance of TMT role structure has long been noted, it has received scant attention until recently when upper-echelons scholars started examining its formation and influence. To stimulate a concerted effort in studying TMT role structure, we develop a framework elaborating its main themes, draw out key contributions from extant research (see the SMS Collection), and outline promising future directions. In particular, we highlight important dynamics of how formal and informal structures complement or compete with each other in the strategic leadership of an organization.

Face Recognition (FR) is increasingly influencing our lives: we use it to unlock our phones; police uses it to identify suspects. Two main concerns are associated with this increase in facial recognition: (1) the fact that these systems are typically less accurate for marginalized groups, which can be described as “bias”, and (2) the increased surveillance through these systems. Our paper is concerned with the first issue. Specifically, we explore an intuitive technique for reducing this bias, namely “blinding” models to sensitive features, such as gender or race, and show why this cannot be equated with reducing bias. Even when not designed for this task, facial recognition models can deduce sensitive features, such as gender or race, from pictures of faces—simply because they are trained to determine the “similarity” of pictures. This means that people with similar skin tones, similar hair length, etc. will be seen as similar by facial recognition models. When confronted with biased decision-making by humans, one approach taken in job application screening is to “blind” the human decision-makers to sensitive attributes such as gender and race by not showing pictures of the applicants. Based on a similar idea, one might think that if facial recognition models were less aware of these sensitive features, the difference in accuracy between groups would decrease. We evaluate this assumption—which has already penetrated into the scientific literature as a valid de-biasing method—by measuring how “aware” models are of sensitive features and correlating this with differences in accuracy. In particular, we blind pre-trained models to make them less aware of sensitive attributes. We find that awareness and accuracy do not positively correlate, i.e., that bias ≠ awareness. In fact, blinding barely affects accuracy in our experiments. The seemingly simple solution of decreasing bias in facial recognition rates by reducing awareness of sensitive features does thus not work in practice: trying to ignore sensitive attributes is not a viable concept for less biased FR.

In this paper, we present the iteration of the multimedia retrieval system vitrivr participating at LSC 2022. vitrivr is a general-purpose retrieval system which has previously participated at LSC. We describe the system architecture and functionality, and show initial results based on the test and validation topics.