Using Model Cards for ethical reflection on machine learning models: an interview-based study

How do tools designed for documenting machine learning models contribute to developers’ ethical reflection? We set out to answer this question regarding Model Cards, a tool proposed for such purpose. We conducted a thematic analysis of eight semi-structured interviews based on speculative design sessions. Each participant assumed the role of developer of an artificial intelligence model in one of two scenarios: loan applications or university admissions. We found evidence that designers may have been selective about which ethical issues – from among those they had reflected on – they recorded in the Model Cards. While participants were hesitant to grant full autonomy to the artifact to be developed, we identified they still tended to rely on a third party (outside the design team) to mediate the relationship between the system and other stakeholders. Our findings contribute to our understanding of documentation tools, their epistemic value, and how they can be leveraged to engage in a more ethically informed design process of artificial intelligence systems

Human-AI Co-Creation Approach to Find Forever Chemicals Replacements

Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replacements for the ``forever chemicals''-- chemicals that enable our modern lives, but are harmful to the environment and the human health. Our approach combines AI capabilities with the domain-specific tacit knowledge of subject matter experts to accelerate the material discovery. Our co-creation process starts with the interaction between the subject matter experts and a generative model that can generate new molecule designs. In this position paper, we discuss our hypothesis that these subject matter experts can benefit from a more iterative interaction with the generative model, asking for smaller samples and ``guiding'' the exploration of the discovery space with their knowledge.Comment: 5 pages, Generative AI and HCI (GenAICHI) Workshop at CHI 23 (ACM CHI Conference on Human Factors in Computing Systems

Localization of gravity on a de Sitter thick braneworld without scalar fields

In this work we present a simple thick braneworld model that is generated by an intriguing interplay between a 5D cosmological constant with a de Sitter metric induced in the 3-brane without the inclusion of scalar fields. We show that 4D gravity is localized on this brane, provide analytic expressions for the massive Kaluza-Klein (KK) fluctuation modes and also show that the spectrum of metric excitations displays a mass gap. We finally present the corrections to Newton's law due to these massive modes. This model has no naked singularities along the fifth dimension despite the existence of a mass gap in the graviton spectrum as it happens in thick branes with 4D Poincare symmetry, providing a simple model with very good features: the curvature is completely smooth along the fifth dimension, it localizes 4D gravity and the spectrum of gravity fluctuations presents a mass gap, a fact that rules out the existence of phenomenologically dangerous ultralight KK excitations in the model. We finally present our solution as a limit of scalar thick branes.Comment: 11 pages in latex, no figures, title and abstract changed, a new section and some references adde

Observation of an Excited Bc+ State

Using pp collision data corresponding to an integrated luminosity of 8.5 fb-1 recorded by the LHCb experiment at center-of-mass energies of s=7, 8, and 13 TeV, the observation of an excited Bc+ state in the Bc+π+π- invariant-mass spectrum is reported. The observed peak has a mass of 6841.2±0.6(stat)±0.1(syst)±0.8(Bc+) MeV/c2, where the last uncertainty is due to the limited knowledge of the Bc+ mass. It is consistent with expectations of the Bc∗(2S31)+ state reconstructed without the low-energy photon from the Bc∗(1S31)+→Bc+γ decay following Bc∗(2S31)+→Bc∗(1S31)+π+π-. A second state is seen with a global (local) statistical significance of 2.2σ (3.2σ) and a mass of 6872.1±1.3(stat)±0.1(syst)±0.8(Bc+) MeV/c2, and is consistent with the Bc(2S10)+ state. These mass measurements are the most precise to date

Bose-Einstein correlations of same-sign charged pions in the forward region in pp collisions at √s=7 TeV

Bose-Einstein correlations of same-sign charged pions, produced in protonproton collisions at a 7 TeV centre-of-mass energy, are studied using a data sample collected by the LHCb experiment. The signature for Bose-Einstein correlations is observed in the form of an enhancement of pairs of like-sign charged pions with small four-momentum difference squared. The charged-particle multiplicity dependence of the Bose-Einstein correlation parameters describing the correlation strength and the size of the emitting source is investigated, determining both the correlation radius and the chaoticity parameter. The measured correlation radius is found to increase as a function of increasing charged-particle multiplicity, while the chaoticity parameter is seen to decreas

Measurement of the inelastic pp cross-section at a centre-of-mass energy of 13TeV

The cross-section for inelastic proton-proton collisions at a centre-of-mass energy of 13TeV is measured with the LHCb detector. The fiducial cross-section for inelastic interactions producing at least one prompt long-lived charged particle with momentum p > 2 GeV/c in the pseudorapidity range 2 < η < 5 is determined to be ϭ acc = 62:2 ± 0:2 ± 2:5mb. The first uncertainty is the intrinsic systematic uncertainty of the measurement, the second is due to the uncertainty on the integrated luminosity. The statistical uncertainty is negligible. Extrapolation to full phase space yields the total inelastic proton-proton cross-section ϭ inel = 75:4 ± 3:0 ± 4:5mb, where the first uncertainty is experimental and the second due to the extrapolation. An updated value of the inelastic cross-section at a centre-of-mass energy of 7TeV is also reported