Research Papers

The prevalence of dementia has increased over time as global life expectancy improves and populations age. An individual's risk of developing dementia is influenced by various genetic, lifestyle, and environmental factors, among others. Predicting dementia risk may enable individuals to employ mitigation strategies or lifestyle changes to delay dementia onset. Current computational approaches to dementia prediction only return risk upon narrow categories of variables and do not account for interactions between different risk variables. The proposed framework utilizes a novel holistic approach to dementia risk prediction and is the first to incorporate various sources of tabular environmental pollution and lifestyle factor data with network systems biology-based genetic data. LightGBM gradient boosting was employed to ensure validity of included factors. This approach successfully models interactions between variables through an original weighted integration method coined Sysable. Multiple machine learning models trained the algorithm to reduce reliance on a single model. The developed approach surpassed all existing dementia risk prediction approaches, with a sensitivity of 85%, specificity of 99%, geometric accuracy of 92%, and AUROC of 91.7%. A transfer learning model was implemented as well. De-biasing algorithms were run on the model via the AI Fairness 360 Library. Effects of demographic disparities on dementia prevalence were analyzed to potentially highlight areas in need and promote equitable and accessible care. The resulting model was additionally integrated into a user-friendly app providing holistic predictions and personalized risk mitigation strategies. The developed model successfully employs holistic computational dementia risk prediction for clinical use.

Recent advances in game informatics have enabled us to find strong strategies across a diverse range of games. However, these strategies are usually difficult for humans to interpret. On the other hand, research in Explainable Artificial Intelligence (XAI) has seen a notable surge in scholarly activity. Interpreting strong or near-optimal strategies or the game itself can provide valuable insights. In this paper, we propose two methods to quantify the feature importance using Shapley values: one for the game itself and another for individual AIs. We empirically show that our proposed methods yield intuitive explanations that resonate with and augment human understanding.

Interpretability and explainability of neural networks is continuously increasing in importance, especially within safety-critical domains and to provide the social right to explanation. Concept based explanations align well with how humans reason, proving to be a good way to explain models. Concept Embedding Models (CEMs) are one such concept based explanation architectures. These have shown to overcome the trade-off between explainability and performance. However, they have a key limitation -- they require concept annotations for all their training data. For large datasets, this can be expensive and infeasible. Motivated by this, we propose Automatic Concept Embedding Models (ACEMs), which learn the concept annotations automatically.

Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems

Sydney Pugh, Ivan Ruchkin, Insup Lee +1

Deep learning models have shown promising predictive accuracy for time-series healthcare applications. However, ensuring the robustness of these models is vital for building trustworthy AI systems. Existing research predominantly focuses on robustness to synthetic adversarial examples, crafted by adding imperceptible perturbations to clean input data. However, these synthetic adversarial examples do not accurately reflect the most challenging real-world scenarios, especially in the context of healthcare data. Consequently, robustness to synthetic adversarial examples may not necessarily translate to robustness against naturally occurring adversarial examples, which is highly desirable for trustworthy AI. We propose a method to curate datasets comprised of natural adversarial examples to evaluate model robustness. The method relies on probabilistic labels obtained from automated weakly-supervised labeling that combines noisy and cheap-to-obtain labeling heuristics. Based on these labels, our method adversarially orders the input data and uses this ordering to construct a sequence of increasingly adversarial datasets. Our evaluation on six medical case studies and three non-medical case studies demonstrates the efficacy and statistical validity of our approach to generating naturally adversarial datasets

Counterfactual Explanations (CEs) are an important tool in Algorithmic Recourse for addressing two questions: 1. What are the crucial factors that led to an automated prediction/decision? 2. How can these factors be changed to achieve a more favorable outcome from a user's perspective? Thus, guiding the user's interaction with AI systems by proposing easy-to-understand explanations and easy-to-attain feasible changes is essential for the trustworthy adoption and long-term acceptance of AI systems. In the literature, various methods have been proposed to generate CEs, and different quality measures have been suggested to evaluate these methods. However, the generation of CEs is usually computationally expensive, and the resulting suggestions are unrealistic and thus non-actionable. In this paper, we introduce a new method to generate CEs for a pre-trained binary classifier by first shaping the latent space of an autoencoder to be a mixture of Gaussian distributions. CEs are then generated in latent space by linear interpolation between the query sample and the centroid of the target class. We show that our method maintains the characteristics of the input sample during the counterfactual search. In various experiments, we show that the proposed method is competitive based on different quality measures on image and tabular datasets -- efficiently returns results that are closer to the original data manifold compared to three state-of-the-art methods, which are essential for realistic high-dimensional machine learning applications.

We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between regulators and industry to create a scalable and cost-efficient mutually beneficial solution. This includes using automated assessments and benchmarks with results transparently communicated through AI cards in an open AI register to facilitate meaningful public comparisons of competing AI systems. Such AI cards should report standardized measures tailored to the specific high-risk applications of AI systems and could be used for conformity assessments under AI transparency and accountability policies such as the European Union's AI Act.

Hate speech moderation remains a challenging task for social media platforms. Human-AI collaborative systems offer the potential to combine the strengths of humans' reliability and the scalability of machine learning to tackle this issue effectively. While methods for task handover in human-AI collaboration exist that consider the costs of incorrect predictions, insufficient attention has been paid to accurately estimating these costs. In this work, we propose a value-sensitive rejection mechanism that automatically rejects machine decisions for human moderation based on users' value perceptions regarding machine decisions. We conduct a crowdsourced survey study with 160 participants to evaluate their perception of correct and incorrect machine decisions in the domain of hate speech detection, as well as occurrences where the system rejects making a prediction. Here, we introduce Magnitude Estimation, an unbounded scale, as the preferred method for measuring user (dis)agreement with machine decisions. Our results show that Magnitude Estimation can provide a reliable measurement of participants' perception of machine decisions. By integrating user-perceived value into human-AI collaboration, we further show that it can guide us in 1) determining when to accept or reject machine decisions to obtain the optimal total value a model can deliver and 2) selecting better classification models as compared to the more widely used target of model accuracy.

This paper argues that training AI systems with absolute constraints -- which forbid certain acts irrespective of the amount of value they might produce -- may make considerable progress on many AI safety problems in principle. First, it provides a guardrail for avoiding the very worst outcomes of misalignment. Second, it could prevent AIs from causing catastrophes for the sake of very valuable consequences, such as replacing humans with a much larger number of beings living at a higher welfare level. Third, it makes systems more corrigible, allowing creators to make corrective interventions in them, such as altering their objective functions or shutting them down. And fourth, it helps systems explore their environment more safely by prohibiting them from exploring especially dangerous acts. I offer a decision-theoretic formalization of an absolute constraints, improving on existing models in the literature, and use this model to prove some results about the training and behavior of absolutist AIs. I conclude by showing that, although absolutist AIs will not maximize expected value, they will not be susceptible to behave irrationally, and they will not (contra coherence arguments) face environmental pressure to become expected-value maximizers.

We address the need to generate faithful explanations of "black box" Deep Learning models. Several tests have been proposed to determine aspects of faithfulness of explanation methods, but they lack cross-domain applicability and a rigorous methodology. Hence, we select an existing test that is model agnostic and is well-suited for comparing one aspect of faithfulness (i.e., sensitivity) of multiple explanation methods, and extend it by specifying formal thresh-olds and building criteria to determine the over-all sensitivity of the explanation method. We present examples of how multiple explanation methods for Convolutional Neural Networks can be compared using this extended methodology. Finally, we discuss the relationship between sensitivity and faithfulness and consider how the test can be adapted to assess different explanation methods in other domains.

Gait analysis, an expanding research area, employs non invasive sensors and machine learning techniques for a range of applicatio ns. In this study, we concentrate on gait analysis for detecting cognitive decline in Parkinson's disease (PD) and under dual task conditions. Using convolutional neural networks (CNNs) and explainable machine learning, we objectively analyze gait data and associate findings with clinically relevant biomarkers. This is accomplished by connecting machine learning outputs to decisions based on human visual observations or derived quantitative gait parameters, which are tested and routinely implemented in curr ent healthcare practice. Our analysis of gait deterioration due to cognitive decline in PD enables robust results using the proposed methods for assessing PD severity from ground reaction force (GRF) data. We achieved classification accuracies of 98% F1 sc ores for each PhysioNet.org dataset and 95.5% F1 scores for the combined PhysioNet dataset. By linking clinically observable features to the model outputs, we demonstrate the impact of PD severity on gait. Furthermore, we explore the significance of cognit ive load in healthy gait analysis, resulting in robust classification accuracies of 100% F1 scores for subject identity verification. We also identify weaker features crucial for model predictions using Layer Wise Relevance Propagation. A notable finding o f this study reveals that cognitive deterioration's effect on gait influences body balance and foot landing/lifting dynamics in both classification cases: cognitive load in healthy gait and cognitive decline in PD gait.

B2B sales requires effective prediction of customer growth, identification of upsell potential, and mitigation of churn risks. LinkedIn sales representatives traditionally relied on intuition and fragmented data signals to assess customer performance. This resulted in significant time investment in data understanding as well as strategy formulation and under-investment in active selling. To overcome this challenge, we developed a data product called Account Prioritizer, an intelligent sales account prioritization engine. It uses machine learning recommendation models and integrated account-level explanation algorithms within the sales CRM to automate the manual process of sales book prioritization. A successful A/B test demonstrated that the Account Prioritizer generated a substantial +8.08% increase in renewal bookings for the LinkedIn Business.

Intelligence is a difficult concept to define, despite many attempts at doing so. Rather than trying to settle on a single definition, this article introduces a broad perspective on what intelligence is, by laying out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them. Within this intelligence space, regions are identified that correspond to both natural -- most particularly, human -- intelligence and artificial intelligence (AI), along with the crossover notion of humanlike intelligence. These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property.