arXiv subjects

Kevin Bauer

Publications and source records attributed to Kevin Bauer.

7 recordsLinked to original sources

arXiv2025

My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions

Amid ongoing policy and managerial debates on keeping humans in the loop of AI decision-making, we investigate whether human involvement in AI-based service production benefits downstream consumers. Partnering with a large savings bank in Europe, we produced pure AI and human-AI collaborative investment advice, passed it to customers, and examined their advice-taking in a field experiment. On the production side, contrary to concerns that humans might inefficiently override AI output, we find that giving a human banker the final say over AI-generated financial advice does not compromise its quality. More importantly, on the consumption side, customers are more likely to follow investment advice from the human-AI collaboration compared to pure AI, especially when facing riskier decisions. In our setting, this increased reliance leads to higher material welfare for consumers. Additional analyses from the field experiment and an online experiment show that the persuasive power of human-AI advice cannot be explained by consumers' beliefs about enhanced advice quality due to human-AI complementarities. Instead, the benefit stems from human involvement acting as a peripheral cue that increases the advice's affective appeal. Our findings suggest that regulations and guidelines should adopt a consumer-centered approach by fostering service environments in which humans and AI systems can collaborate to improve consumer outcomes. These insights are relevant for managers designing AI-based services and for policymakers advocating for human oversight in AI systems.

econ.GN↗

arXiv2025

Beware of "Explanations" of AI

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations.

cs.LG↗

arXiv2021

Learning to Isolate Muons

Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider physics data. We explore whether there is information available in calorimeter deposits that is not captured by the standard approach of isolation cones. We find that convolutional networks and particle-flow networks accessing the calorimeter cells surpass the performance of isolation cones, suggesting that the radial energy distribution and the angular structure of the calorimeter deposits surrounding the muon contain unused discrimination power. We assemble a small set of high-level observables which summarize the calorimeter information and close the performance gap with networks which analyze the calorimeter cells directly. These observables are theoretically well-defined and can be studied with collider data.

physics.data-an↗

arXiv2016

Searching for spin-3/2 leptons

A model of spin-3/2 vector-like leptons is introduced, which serve as an alternative probe of possible lepton compositeness. Possible signatures at the Large Hadron Collider are explored, and current data are used to constrain the masses of the hypothetical new leptons.

hep-ph↗

arXiv2016

Jet Substructure Classification in High-Energy Physics with Deep Neural Networks

At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a two-dimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally-connected and fully-connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.

hep-ex↗

arXiv2015

Mono-Z': searches for dark matter in events with a resonance and missing transverse energy

We analyze the potential dark matter implications of LHC events with missing transverse momentum and a resonance, such as a Z', decaying to a pair of jets or leptons. This final state contains significant discovery potential, but has not yet been examined in detail by the LHC experiments. We introduce models of Z' production in association with dark matter particles, propose reconstruction and selection strategies, and estimate the sensitivity of the current LHC dataset.

hep-ph↗

arXiv2013

A measurement of the energy and timing resolution of GlueX Forward Calorimeter using an electron beam

The performance of the GlueX Forward Calorimeter was studied using a small version of the detector and a variable energy electron beam derived from the Hall B tagger at Jefferson Lab. For electron energies from 110 MeV to 260 MeV, which are near the lower-limits of the design sensitivity, the fractional energy resolution was measured to range from 20% to 14%, which meets the design goals. The use of custom 250 MHz flash ADCs for readout allowed precise measurements of signal arrival times. The detector achieved timing resolutions of 0.38 ns for a single 100 mV pulse, which will allow timing discrimination of photon beam bunches and out-of-time background during the operation of the GlueX detector.

physics.ins-det↗