Trustworthy Artificial Intelligence

Trust or trustworthiness are hard to define. There are many aspects that can increase or decrease the trust in an Artificial Intelligence systems. This is why entities such as the High-level expert group on AI (HLEG) and the European commission’s artificial intelligence act are putting forward guidelines and regulations demand trustworthiness and help to better define it. One aspect that can increase the trust in a system is to make the system more transparent. For AI systems this can be achieved through Explainable AI or XAI which has the goal to explain learning systems. This article will list some requirements from the HLEG and the European artificial intelligence act and will go further into transparency and how it can be achieved through explanations. At the end we will cover personalized explanations, how they could be achieved and how they could benefit users

Personalized Explanations

Machine learning systems are often hard to investigate and intransparent in their decision making . Explainable Artificial Intelligence (XAI) tries to make these systems more transparent. However, most work in the field focuses on technical aspects like maximizing metrics. The human aspects of explainability are often neglected. In this work, we present personalized explanations, which instead focus on the user. Personalized explanations can be adapted to individual users to be as useful and relevant as possible. They can be interacted with to give users the ability to engage in an explanatory dialog with the system. Finally, they should also protect user data to increase the trust in the explanation system

Absorptive capacity and the growth and investment effects of regional transfers : a regression discontinuity design with heterogeneous treatment effects

Researchers often estimate average treatment effects of programs without investigating heterogeneity across units. Yet, individuals, firms, regions, or countries vary in their ability, e.g., to utilize transfers. We analyze Objective 1 Structural Funds transfers of the European Commission to regions of EU member states below a certain income level by way of a regression discontinuity design with systematically heterogeneous treatment effects. Only about 30% and 21% of the regions - those with sufficient human capital and good-enough institutions - are able to turn transfers into faster per-capita income growth and per-capita investment. In general, the variance of the treatment effect is much bigger than its mean

Going NUTS: The Effect of EU Structural Funds on Regional Performance

The European Union (EU) provides grants to disadvantaged regions of member states to allow them to catch up with the EU average. Under the Objective 1 scheme, NUTS2 regions with a GDP per capita level below 75% of the EU average qualify for structural funds transfers from the central EU budget. This rule gives rise to a regression-discontinuity design that exploits the discrete jump in the probability of EU transfer receipt at the 75% threshold. Additional variability arises for smaller regional aggregates - so-called NUTS3 regions - which are nested in a NUTS2 mother region. Whereas some relatively rich NUTS3 regions may receive EU funds because their NUTS2 mother region qualifies, other relatively poor NUTS3 regions may not receive EU funds because their NUTS2 mother region does not qualify. We find positive growth effects of Objective 1 funds, but no employment effects. A simple cost-benefit calculation suggests that Objective 1 transfers are not only effective, but also cost-efficient.structural funds, regional growth, regression discontinuity design, quasi-randomized experiment

Explainable Artificial Intelligence for Interpretable Data Minimization

Black box models such as deep neural networks are increasingly being deployed in high-stakes fields, including justice, health, and finance. Furthermore, they require a huge amount of data, and such data often contains personal information. However, the principle of data minimization in the European Union’s General Data Protection Regulation requires collecting only the data that is essential to fulfilling a particular purpose. Implementing data minimization for black box models can be difficult because it involves identifying the minimum set of variables that are relevant to the model’s prediction, which may not be apparent without access to the model’s inner workings. In addition, users are often reluctant to share all their personal information. We propose an interactive system to reduce the amount of personal data by determining the minimal set of features required for a correct prediction using explainable artificial intelligence techniques. Our proposed method can inform the user whether the provided variables contain enough information for the model to make accurate predictions or if additional variables are necessary. This humancentered approach can enable providers to minimize the amount of personal data collected for analysis and may increase the user’s trust and acceptance of the system

Computational IR spectroscopy of interfacial water at fluorinated and non-fluorinated hydrophobic surfaces

We report ab-initio simulations of the interface between water and fluorinated and non-fluorinated hydrocarbon self assembled monolayers (SAMs) and compare with the prototypical interfacial system, the vapor-water interface. The thickness of the microscopic depletion layer between SAMs and water is larger for the fluorinated SAM, consistent with the larger contact angle of fluorinated SAMs. We calculate the infrared absorption spectrum of interfacial water, which displays a prominent sharp peak at around 3700 $\mathrm{cm}^{-1}$, signaling the presence of dangling OH bonds. We describe the vibrational properties of dangling OH bonds by a harmonic model and show that spectral line shifts reflect OH-dangling-bond interactions with the surface and line widths report on the rotational lifetimes of dangling OH configurations