arXiv subjects

Max Schemmer

Publications and source records attributed to Max Schemmer.

At least 19 recordsLinked to original sources

arXiv2025

Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition

In recent years, the rapid development of AI systems has brought about the benefits of intelligent services but also concerns about security and reliability. By fostering appropriate user reliance on an AI system, both complementary team performance and reduced human workload can be achieved. Previous empirical studies have extensively analyzed the impact of factors ranging from task, system, and human behavior on user trust and appropriate reliance in the context of one-step decision making. However, user reliance on AI systems in tasks with complex semantics that require multi-step workflows remains under-explored. Inspired by recent work on task decomposition with large language models, we propose to investigate the impact of a novel Multi-Step Transparent (MST) decision workflow on user reliance behaviors. We conducted an empirical study (N = 233) of AI-assisted decision making in composite fact-checking tasks (i.e., fact-checking tasks that entail multiple sub-fact verification steps). Our findings demonstrate that human-AI collaboration with an MST decision workflow can outperform one-step collaboration in specific contexts (e.g., when advice from an AI system is misleading). Further analysis of the appropriate reliance at fine-grained levels indicates that an MST decision workflow can be effective when users demonstrate a relatively high consideration of the intermediate steps. Our work highlights that there is no one-size-fits-all decision workflow that can help obtain optimal human-AI collaboration. Our insights help deepen the understanding of the role of decision workflows in facilitating appropriate reliance. We synthesize important implications for designing effective means to facilitate appropriate reliance on AI systems in composite tasks, positioning opportunities for the human-centered AI and broader HCI communities.

cs.AI↗

arXiv2025

Photon Transport in a Gas of Two-Level Atoms: Unveiling Quantum Light Creation

We present a theoretical analysis of nearly monochromatic light propagation through a gas of two-level atoms using the Heisenberg-Langevin equation method. Our focus is on the evolution of the photon annihilation operator and its impact on the second-order correlation function, $g^{(2)}({\tau})$, with particular emphasis on photon antibunching behavior. The model accounts for both open and closed atomic system approximations, including Doppler broadening and the influence of pump field detuning. We derive expressions that reproduce known results from scattering theory and extend the analysis to complex systems, such as inhomogeneously broadened media. The theoretical predictions are compared with experimental data from a waveguide QED platform, which show good agreement and thereby demonstrate the power of our approach. Future work will explore extensions to even more complex systems and other quantum light characteristics for practical applications.

quant-ph↗

arXiv2024

Emergence of unidirectionality and phase separation in optically dense emitter ensembles

The transmission of light through an ensemble of two-level emitters in a one-dimensional geometry is commonly described by one of two emblematic models of quantum electrodynamics (QED): the driven-dissipative Dicke model or the Maxwell-Bloch equations. Both exhibit distinct features of phase transitions and phase separations, depending on system parameters such as optical depth and external drive strength. Here, we explore the crossover between these models via a parent spin model from bidirectional waveguide QED, by varying positional disorder among emitters. Solving mean-field equations and employing a second-order cumulant expansion for the unidirectional model -- equivalent to the Maxwell-Bloch equations -- we study phase diagrams, the emitter's inversion, and transmission depending on optical depth, drive strength, and spatial disorder. We find in the thermodynamic limit the emergence of phase separation with a critical value that depends on the degree of spatial order but is independent of Doppler broadening effects. Even far from the thermodynamic limit, this critical value marks a special point in the emitter's correlation landscape of the unidirectional model and is also observed as a maximum in the magnitude of inelastically transmitted photons. We conclude that a large class of effective one-dimensional systems without tight control of the emitter's spatial ordering can be effectively modeled using a unidirectional waveguide approach.

quant-ph↗

arXiv2024

Simple analytical model describing the collective nonlinear response of an ensemble of two-level emitters weakly coupled to a waveguide

We model and investigate the collective nonlinear optical response of an ensemble of two-level emitters that are weakly coupled to a single-mode waveguide. Our approach generalizes the insight that photon-photon correlations in the light scattered by a single two-level emitter result from two-photon interference to the case of many emitters. Using our model, we study different configurations for probing the nonlinear response of the ensemble, e.g., through the waveguide or via external illumination, and derive analytical expressions for the second-order quantum coherence function, $g^{(2)}(\tau)$, as well as for the squeezing spectrum of the output light in the waveguide, $S_\theta(\omega)$. For the transmission of resonant guided light, we recover the same predictions as previously made with far more involved theoretical models when analyzing experimental results regarding $g^{(2)}(\tau)$ (Prasad et al. [1]) and $S_\theta(\omega)$ (Hinney et al. [2]). We also study the transmission of light that is detuned from the transition of the two-level emitter, a situation that we recently studied experimentally (Cordier et al. [3]). Our model predictions show how the collectively enhanced nonlinear response of weakly coupled emitters can be harnessed to generate non-classical states of light using ensembles ranging from a few to many emitters.

quant-ph↗

arXiv2024

Magic-wavelength nanofiber-based two-color dipole trap with sub-$\lambda/2$ spacing

We report on the realization and characterization of a novel magic-wavelength nanofiber-based two-color optical dipole trap for cesium that allows us to generate two diametral periodic one-dimensional arrays of trapping sites with a spacing significantly smaller than half the resonant free-space wavelength of the cesium D2 transition. This is achieved by launching a blue-detuned partial standing wave and two red-detuned light fields through the nanofiber. We trap and optically interface the atoms in the resulting periodic optical potential and characterize the trap by measuring the lifetime of the trapped atoms, the atom-light coupling strength, the filling factor, and the trap frequencies in the radial and axial directions. The implementation of this nanofiber-based optical interface with magic trapping wavelengths and sub-$\lambda/2$ spacing is an important step towards the exploration of novel collective radiative effects, such as selective radiance.

physics.atom-ph↗

arXiv2024

Topological photon pumping in quantum optical systems

We establish the concept of topological pumping in one-dimensional systems with long-range couplings and apply it to the transport of a photon in quantum optical systems. In our theoretical investigation, we introduce an extended version of the Rice-Mele model with all-to-all couplings. By analyzing its properties, we identify the general conditions for topological pumping and theoretically and numerically demonstrate topologically protected and dispersionless transport of a photon on a one-dimensional emitter chain. As concrete examples, we investigate three different popular quantum optics platforms, namely Ryd\-berg atom lattices, dense lattices of atoms excited to low-lying electronic states, and atoms coupled to waveguides, using experimentally relevant parameters. We observe that despite the long-ranged character of the dipole-dipole interactions, topological pumping facilitates the transport of a photon with a fidelity per cycle which can reach 99.9\%. Moreover, we find that the photon pumping process remains topologically protected against local disorder in the coupling parameters.

quant-ph↗

arXiv2024

Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence

Artificial intelligence (AI) has the potential to significantly enhance human performance across various domains. Ideally, collaboration between humans and AI should result in complementary team performance (CTP) -- a level of performance that neither of them can attain individually. So far, however, CTP has rarely been observed, suggesting an insufficient understanding of the principle and the application of complementarity. Therefore, we develop a general concept of complementarity and formalize its theoretical potential as well as the actual realized effect in decision-making situations. Moreover, we identify information and capability asymmetry as the two key sources of complementarity. Finally, we illustrate the impact of each source on complementarity potential and effect in two empirical studies. Our work provides researchers with a comprehensive theoretical foundation of human-AI complementarity in decision-making and demonstrates that leveraging these sources constitutes a viable pathway towards designing effective human-AI collaboration, i.e., the realization of CTP.

cs.HC↗

arXiv2023

Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.

cs.AI↗

arXiv2023

Conceptualizing A Multi-Sided Platform For Cloud Computing Resource Trading

Cost-effective and responsible use of cloud computing resources (CCR) is on the business agenda of many companies. Despite this strategic goal, two geopolitical strategy decisions mainly influence the continuous existence of overcapacity: Europe's General Data Protection Regulation and the US's Cloud Act. Given the circumstances, a typical data center produces approximately 30% overcapacity annually. This overcapacity has severe environmental and economic consequences. Our work addresses this overcapacity by proposing a multi-sided platform for CCR trading. We initiate our research by conducting a literature review to explore the existing body of knowledge which indicates a lack of recent and evaluated platform design knowledge for CCR trading. We address this research gap by deriving design requirements and design principles. We instantiate and evaluate the design knowledge in a respective platform framework. Thus, we contribute to research and practice by deriving and evaluating design knowledge and proposing a platform framework.

cs.CY↗

arXiv2023

Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations

AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as to whether actually follow that advice: they have to "appropriately" rely on correct and turn down incorrect advice. However, current research on appropriate reliance still lacks a common definition as well as an operational measurement concept. Additionally, no in-depth behavioral experiments have been conducted that help understand the factors influencing this behavior. In this paper, we propose Appropriateness of Reliance (AoR) as an underlying, quantifiable two-dimensional measurement concept. We develop a research model that analyzes the effect of providing explanations for AI advice. In an experiment with 200 participants, we demonstrate how these explanations influence the AoR, and, thus, the effectiveness of AI advice. Our work contributes fundamental concepts for the analysis of reliance behavior and the purposeful design of AI advisors.

cs.AI↗

arXiv2023

Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by considering both parties' capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams -- compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when they are aware that an AI model delegated task instances to them. In an experimental study with 196 participants, we show that task performance and task satisfaction improve through AI delegation, regardless of whether humans are aware of the delegation. Additionally, we identify humans' increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces.

cs.HC↗

arXiv2023

Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series

Detecting rare events is essential in various fields, e.g., in cyber security or maintenance. Often, human experts are supported by anomaly detection systems as continuously monitoring the data is an error-prone and tedious task. However, among the anomalies detected may be events that are rare, e.g., a planned shutdown of a machine, but are not the actual event of interest, e.g., breakdowns of a machine. Therefore, human experts are needed to validate whether the detected anomalies are relevant. We propose to support this anomaly investigation by providing explanations of anomaly detection. Related work only focuses on the technical implementation of explainable anomaly detection and neglects the subsequent human anomaly investigation. To address this research gap, we conduct a behavioral experiment using records of taxi rides in New York City as a testbed. Participants are asked to differentiate extreme weather events from other anomalous events such as holidays or sporting events. Our results show that providing counterfactual explanations do improve the investigation of anomalies, indicating potential for explainable anomaly detection in general.

cs.LG↗

arXiv2022

Tailoring photon statistics with an atom-based two-photon interferometer

Controlling the photon statistics of light is paramount for quantum science and technologies. Recently, we demonstrated that transmitting resonant laser light past an ensemble of two-level emitters can result in a stream of single photons or excess photon pairs. This transformation is due to quantum interference between the transmitted and incoherently scattered two-photon component. Here, using the dispersion of the atomic medium, we actively control the relative quantum phase between these two components. We thereby realize a tunable two-photon interferometer and observe interference fringes in the normalized photon coincidence rate, varying from antibunching to bunching. Beyond the fundamental insight that the quantum phase between incoherent and coherent light can be tuned and dictates photon statistics, our results lend themselves to the development of novel quantum light sources.

quant-ph↗

arXiv2022

Higher-order mean-field theory of chiral waveguide QED

Waveguide QED with cold atoms provides a potent platform for the study of non-equilibrium, many-body, and open-system quantum dynamics. Even with weak coupling and strong photon loss, the collective enhancement of light-atom interactions leads to strong correlations of photons arising in transmission, as shown in recent experiments. Here we apply an improved mean-field theory based on higher-order cumulant expansions to describe the experimentally relevant, but theoretically elusive, regime of weak coupling and strong driving of large ensembles. We determine the transmitted power, squeezing spectra and the degree of second-order coherence, and systematically check the convergence of the results by comparing expansions that truncate cumulants of few-particle correlations at increasing order. This reveals the important role of many-body and long-range correlations between atoms in steady state. Our approach allows to quantify the trade-off between anti-bunching and output power in previously inaccessible parameter regimes. Calculated squeezing spectra show good agreement with measured data, as we present here.

quant-ph↗

arXiv2022

Will a single two-level atom simultaneously scatter two photons?

The interaction of light with a single two-level emitter is the most fundamental process in quantum optics, and is key to many quantum applications. As a distinctive feature, two photons are never detected simultaneously in the light scattered by the emitter. This is commonly interpreted by saying that a single two-level quantum emitter can only absorb and emit single photons. However, it has been theoretically proposed that the photon anti-correlations can be thought to arise from quantum interference between two possible two-photon scattering amplitudes, which one refers to as coherent and incoherent. This picture is in stark contrast to the aforementioned one, in that it assumes that the atom even has two different mechanisms at its disposal to scatter two photons at the same time. Here, we validate the interference picture by experimentally verifying the 40-year-old conjecture that, by spectrally rejecting only the coherent component of the fluorescence light of a single two-level atom, the remaining light consists of photon pairs that have been simultaneously scattered by the atom. Our results offer fundamental insights into the quantum-mechanical interaction between light and matter and open up novel approaches for the generation of highly non-classical light fields.

quant-ph↗

arXiv2022

A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decision-making performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users' performance. Additionally, the first results indicate that human-AI decision-making tends to yield better task performance on text data. However, we find no effect of explanations on users' performance compared to sole AI predictions. Our initial synthesis gives rise to future research investigating the underlying causes and contributes to further developing algorithms that effectively benefit human decision-makers by providing meaningful explanations.

cs.HC↗

arXiv2022

On the Effect of Information Asymmetry in Human-AI Teams

Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas. Teaming between AI and humans may even lead to complementary team performance (CTP), i.e., a level of performance beyond the ones that can be reached by AI or humans individually. Many researchers have proposed using explainable AI (XAI) to enable humans to rely on AI advice appropriately and thereby reach CTP. However, CTP is rarely demonstrated in previous work as often the focus is on the design of explainability, while a fundamental prerequisite -- the presence of complementarity potential between humans and AI -- is often neglected. Therefore, we focus on the existence of this potential for effective human-AI decision-making. Specifically, we identify information asymmetry as an essential source of complementarity potential, as in many real-world situations, humans have access to different contextual information. By conducting an online experiment, we demonstrate that humans can use such contextual information to adjust the AI's decision, finally resulting in CTP.

cs.HC↗

arXiv2022

On the Influence of Explainable AI on Automation Bias

Artificial intelligence (AI) is gaining momentum, and its importance for the future of work in many areas, such as medicine and banking, is continuously rising. However, insights on the effective collaboration of humans and AI are still rare. Typically, AI supports humans in decision-making by addressing human limitations. However, it may also evoke human bias, especially in the form of automation bias as an over-reliance on AI advice. We aim to shed light on the potential to influence automation bias by explainable AI (XAI). In this pre-test, we derive a research model and describe our study design. Subsequentially, we conduct an online experiment with regard to hotel review classifications and discuss first results. We expect our research to contribute to the design and development of safe hybrid intelligence systems.

cs.HC↗