Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices - Arias-Cabarcos, Patricia; Fallahi, Matin; Habrich, Thilo; Schulze, Karen; Becker, Christian; Strufe, Thorsten (ACM Transactions on Privacy and Security)
Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.
Keywords: Safety; Risk; Reliability and Quality; General Computer Science

Run-to-Failure Relay Dataset for Predictive Maintenance Research With Machine Learning - Winkel, Fabian; Deuse-Kleinsteuber, Johannes; Böcker, Joachim (IEEE Transactions on Reliability)
Keywords: Electrical and Electronic Engineering; Safety; Risk; Reliability and Quality

A model of cycles and bubbles under heterogeneous beliefs in financial markets - Burs, Carina (Cogent Economics & Finance)
I study the effect of heterogeneous beliefs about asset prices on the long-term behavior of financial markets. Starting from the ideas of Abreu and Brunnermeier (Citation2003), a two-dimensional system of differential equations is developed. The first dynamic variable is the asset price growth rate. The second dynamic variable is the number of investors who believe that asset prices are abnormally high. In a phase plane analysis, I find both stable and unstable equilibria, depending on the spread of information and the response to other agents’ beliefs. If individuals try to increase their returns while perceiving more overpricing, these equilibria can be spirals or even approach limit cycles. Although I intend to study general price patterns, abnormally high asset prices can be caused by financial bubbles. In this model, bubbles can emerge and deflate both in cycles or directly, or they can grow until they burst. Further, I analyze market behavior after a central bank increases the interest rate. This can lead to new stable equilibria, but the emergence and bursting of bubbles cannot be prevented.
Keywords: asset pricing; subjective information; stability conditions; monetary policy; risk aversion

Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time - Leffrang, Dirk; Bösch, Kevin; Müller, Oliver (Hawaii International Conference on System Sciences)
Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.
Keywords: Algorithm aversion; Time series; Decision making; Advice taking; Forecasting

AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization - Leffrang, Dirk (International Conference on Information Systems)
Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice.
Keywords: Artificial Intelligence; Algorithm Appreciation; Framing; Advice-taking; Expertise

The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization - Leffrang, Dirk (Wirtschaftsinformatik Conference)
Despite the widespread use of machine learning algorithms, their effectiveness is limited by a phenomenon known as algorithm aversion. Recent research concluded that unobserved variables can cause algorithm aversion. However, the impact of an unobserved variable on algorithm aversion remains unclear. Previous studies focused on situations where humans had more variables available than algorithms. We extend this research by conducting an online experiment with 94 participants, systematically varying the number of observable variables to the advisor and the advisor type. Surprisingly, our results did not confirm that an unobserved variable had a negative effect on advice-taking. Instead, we found a positive impact in an algorithm appreciation scenario. This study provides new insights into the paradoxical behavior in which people weigh advice more despite having fewer variables, as they correct for the advisor's errors. Practitioners should consider this behavior when designing algorithms and account for user correction behavior.
Keywords: Algorithm aversion; Data; Decision-making; Advice-taking; Human-Computer Interaction

Business Reputation Systems based on Blockchain Technology—A Risky Advance - Hemmrich, Simon (Proceedings of 31st European Conference on Information Systems (ECIS 2023))
Reputation is indispensable for online business since it supports customers in their buying decisions and allows sellers to justify premium prices. While IS research has investigated reputation systems mainly as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a transaction is performed, customers commit to pay a bonus that is granted if a service provider has performed a service properly. As opposed to rival reputation systems that build on cumulated ratings or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online transactions. We expect this system class to provide more trustworthy ratings, which might reduce agency costs and serve quality providers to establish a reputation towards new customers, building on second-order trust.
Keywords: Trust; Risk; Reputation System; Blockchain Technology; Business Reputation System.

Computation on Encrypted Data Using Dataflow Authentication - Fischer, Andreas; Fuhry, Benny; Kußmaul, Jörn; Janneck, Jonas; Kerschbaum, Florian; Bodden, Eric (ACM Transactions on Privacy and Security)
Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks. However, due to its high computational complexity, its applicability to general-purpose computing remains limited. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data. We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described previously. We implemented two flavors of DFAuth, a Java bytecode-to-bytecode compiler, and an SGX enclave running a small and program-independent trusted code base. We applied DFAuth to a neural network performing machine learning on sensitive medical data and a smart charging scheduler for electric vehicles. Our transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in \( 12.55 \,ms \) . Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds.
Keywords: Safety; Risk; Reliability and Quality; General Computer Science

Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall - Letmathe, Sebastian; Feng, Yuanhua; Uhde, André (Journal of Risk)
In this paper new semiparametric generalized autoregressive conditional heteroscedasticity (GARCH) models with long memory are introduced. A multiplicative decomposition of the volatility into a conditional component and an unconditional component is assumed. The estimation of the latter is carried out by means of a data-driven local polynomial smoother. According to the revised recommendations by the Basel Committee on Banking Supervision to measure market risk in the banks’ trading books, these new semiparametric GARCH models are applied to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets. Standard regulatory traffic-light tests and a newly introduced traffic-light test for the ES are carried out for all models. In addition, model performance is assessed via a recently introduced model selection criterion. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long-memory GARCH models are a meaningful substitute for their conventional, parametric counterparts.
Keywords: long memory; generalized autoregressive conditional heteroscedasticity (GARCH) models; value-at-risk (VaR); expected shortfall (ES); traffic-light test; backtesting

Decision-making under Imperfect Information with Bayesian Learning or Heuristic Rules - Burs, Carina; Gries, Thomas
Information is one of the most important ingredients for decision-making. While the neoclassical assumption of perfect information is surely an important conceptual benchmark for discussing efficient allocations, it is obviously far from describing a rational choice under real conditions. In reality, optimal choices should be considered choices under imperfect information. Thus, decision-makers' information problem can be solved by two strategies. Either they collect an optimal set of information to make an optimal allocation choice under this imperfect information set or they can apply heuristic reasoning. In this paper, we suggest a formal model framework for the example of a simple consumer decision for the allocation of differentiated goods to explore information acquisition strategies in such a simple standard choice situation. Using the model variation under perfect information as a benchmark, we answer the following questions. First and most importantly, under imperfect information, can a heuristic rule substitute information acquisition as an optimal choice? Second, what is the role of risk aversion in the information acquisition process? Finally, we explore the differences to the benchmark, both ex ante the first purchase decision and ex post when repeated purchases and consumption allows for experiences with the choices made.
Keywords: information economics; imperfect information; Bayesian learning; risk; heuristics; differentiated products