Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms

Corporate influence and the academic computer science discipline. [4: CMU]

Prosopographical work on the four major centers for computer research in the United States has now been conducted, resulting in big questions about the independence of, so called, computer science

Scaling Laws Do Not Scale

Recent work has proposed a power law relationship, referred to as ``scaling laws,'' between the performance of artificial intelligence (AI) models and aspects of those models' design (e.g., dataset size). In other words, as the size of a dataset (or model parameters, etc) increases, the performance of a given model trained on that dataset will correspondingly increase. However, while compelling in the aggregate, this scaling law relationship overlooks the ways that metrics used to measure performance may be precarious and contested, or may not correspond with how different groups of people may perceive the quality of models' output. In this paper, we argue that as the size of datasets used to train large AI models grows, the number of distinct communities (including demographic groups) whose data is included in a given dataset is likely to grow, each of whom may have different values. As a result, there is an increased risk that communities represented in a dataset may have values or preferences not captured by (or in the worst case, at odds with) the metrics used to evaluate model performance for scaling laws. We end the paper with implications for AI scaling laws -- that models may not, in fact, continue to improve as the datasets get larger -- at least not for all people or communities impacted by those models

On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits

We consider the nonstochastic multi-agent multi-armed bandit problem with agents collaborating via a communication network with delays. We show a lower bound for individual regret of all agents. We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph. We also show that an FTRL algorithm with a suitable regularizer is regret optimal with respect to the scaling with the edge-delay parameter. We present numerical experiments validating our theoretical results and demonstrate cases when our algorithms outperform previously proposed algorithms.Comment: Published in AAMAS 202

Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods

Proceedings of the Weizenbaum Conference 2023: AI, Big Data, Social Media, and People on the Move

The conference focused on topics that arise from artificial intelligence (AI) and Big Data deployed on and used by 'people on the move'. We understand the term 'people on the move' in a broad sense: individuals and groups who - by volition or necessity - are changing their lives and/or their structural position in societies. This encompasses the role of automated systems or AI in different forms of geographical and social change, including migration and labour mobility, algorithmic uses of 'location', as well as discourses of and about people on the move

Operationalizing fairness for responsible machine learning

As machine learning (ML) is increasingly used for decision making in scenarios that impact humans, there is a growing awareness of its potential for unfairness. A large body of recent work has focused on proposing formal notions of fairness in ML, as well as approaches to mitigate unfairness. However, there is a growing disconnect between the ML fairness literature and the needs to operationalize fairness in practice. This thesis addresses the need for responsible ML by developing new models and methods to address challenges in operationalizing fairness in practice. Specifically, it makes the following contributions. First, we tackle a key assumption in the group fairness literature that sensitive demographic attributes such as race and gender are known upfront, and can be readily used in model training to mitigate unfairness. In practice, factors like privacy and regulation often prohibit ML models from collecting or using protected attributes in decision making. To address this challenge we introduce the novel notion of computationally-identifiable errors and propose Adversarially Reweighted Learning (ARL), an optimization method that seeks to improve the worst-case performance over unobserved groups, without requiring access to the protected attributes in the dataset. Second, we argue that while group fairness notions are a desirable fairness criterion, they are fundamentally limited as they reduce fairness to an average statistic over pre-identified protected groups. In practice, automated decisions are made at an individual level, and can adversely impact individual people irrespective of the group statistic. We advance the paradigm of individual fairness by proposing iFair (individually fair representations), an optimization approach for learning a low dimensional latent representation of the data with two goals: to encode the data as well as possible, while removing any information about protected attributes in the transformed representation. Third, we advance the individual fairness paradigm, which requires that similar individuals receive similar outcomes. However, similarity metrics computed over observed feature space can be brittle, and inherently limited in their ability to accurately capture similarity between individuals. To address this, we introduce a novel notion of fairness graphs, wherein pairs of individuals can be identified as deemed similar with respect to the ML objective. We cast the problem of individual fairness into graph embedding, and propose PFR (pairwise fair representations), a method to learn a unified pairwise fair representation of the data. Fourth, we tackle the challenge that production data after model deployment is constantly evolving. As a consequence, in spite of the best efforts in training a fair model, ML systems can be prone to failure risks due to a variety of unforeseen reasons. To ensure responsible model deployment, potential failure risks need to be predicted, and mitigation actions need to be devised, for example, deferring to a human expert when uncertain or collecting additional data to address model’s blind-spots. We propose Risk Advisor, a model-agnostic meta-learner to predict potential failure risks and to give guidance on the sources of uncertainty inducing the risks, by leveraging information theoretic notions of aleatoric and epistemic uncertainty. This dissertation brings ML fairness closer to real-world applications by developing methods that address key practical challenges. Extensive experiments on a variety of real-world and synthetic datasets show that our proposed methods are viable in practice.Mit der zunehmenden Verwendung von Maschinellem Lernen (ML) in Situationen, die Auswirkungen auf Menschen haben, nimmt das Bewusstsein über das Potenzial für Unfair- ness zu. Ein großer Teil der jüngeren Forschung hat den Fokus auf das formale Verständnis von Fairness im Zusammenhang mit ML sowie auf Ansätze zur Überwindung von Unfairness gelegt. Jedoch driften die Literatur zu Fairness in ML und die Anforderungen zur Implementierung in der Praxis zunehmend auseinander. Diese Arbeit beschäftigt sich mit der Notwendigkeit für verantwortungsvolles ML, wofür neue Modelle und Methoden entwickelt werden, um die Herausforderungen im Fairness-Bereich in der Praxis zu bewältigen. Ihr wissenschaftlicher Beitrag ist im Folgenden dargestellt. In Kapitel 3 behandeln wir die Schlüsselprämisse in der Gruppenfairnessliteratur, dass sensible demografische Merkmale wie etwa die ethnische Zugehörigkeit oder das Geschlecht im Vorhinein bekannt sind und während des Trainings eines Modells zur Reduzierung der Unfairness genutzt werden können. In der Praxis hindern häufig Einschränkungen zum Schutz der Privatsphäre oder gesetzliche Regelungen ML-Modelle daran, geschützte Merkmale für die Entscheidungsfindung zu sammeln oder zu verwenden. Um diese Herausforderung zu überwinden, führen wir das Konzept der Komputational-identifizierbaren Fehler ein und stellen Adversarially Reweighted Learning (ARL) vor, ein Optimierungsverfahren, das die Worst-Case-Performance bei unbekannter Gruppenzugehörigkeit ohne Wissen über die geschützten Merkmale verbessert. In Kapitel 4 stellen wir dar, dass Konzepte für Gruppenfairness trotz ihrer Eignung als Fairnesskriterium grundsätzlich beschränkt sind, da Fairness auf eine gemittelte statistische Größe für zuvor identifizierte geschützte Gruppen reduziert wird. In der Praxis werden automatisierte Entscheidungen auf einer individuellen Ebene gefällt, und können unabhängig von der gruppenbezogenen Statistik Nachteile für Individuen haben. Wir erweitern das Konzept der individuellen Fairness um unsere Methode iFair (individually fair representations), ein Optimierungsverfahren zum Erlernen einer niedrigdimensionalen Darstellung der Daten mit zwei Zielen: die Daten so akkurat wie möglich zu enkodieren und gleichzeitig jegliche Information über die geschützten Merkmale in der transformierten Darstellung zu entfernen. In Kapitel 5 entwickeln wir das Paradigma der individuellen Fairness weiter, das ein ähnliches Ergebnis für ähnliche Individuen erfordert. Ähnlichkeitsmetriken im beobachteten Featureraum können jedoch unzuverlässig und inhärent beschränkt darin sein, Ähnlichkeit zwischen Individuen korrekt abzubilden. Um diese Herausforderung anzugehen, führen wir den neue Konzept der Fairnessgraphen ein, in denen Paare (oder Sets) von Individuen als ähnlich im Bezug auf die ML-Aufgabe identifiziert werden. Wir übersetzen das Problem der individuellen Fairness in eine Grapheinbindung und stellen PFR (pairwise fair representations) vor, eine Methode zum Erlernen einer vereinheitlichten paarweisen fairen Abbildung der Daten. In Kapitel 6 gehen wir die Herausforderung an, dass sich die Daten im Feld nach der Inbetriebnahme des Modells fortlaufend ändern. In der Konsequenz können ML-Systeme trotz größter Bemühungen, ein faires Modell zu trainieren, aufgrund einer Vielzahl an unvorhergesehenen Gründen scheitern. Um eine verantwortungsvolle Implementierung sicherzustellen, gilt es, Risiken für ein potenzielles Versagen vorherzusehen und Gegenmaßnahmen zu entwickeln,z.B. die Übertragung der Entscheidung an einen menschlichen Experten bei Unsicherheit oder das Sammeln weiterer Daten, um die blinden Flecken des Modells abzudecken. Wir stellen mit Risk Advisor einen modell-agnostischen Meta-Learner vor, der Risiken für potenzielles Versagen vorhersagt und Anhaltspunkte für die Ursache der zugrundeliegenden Unsicherheit basierend auf informationstheoretischen Konzepten der aleatorischen und epistemischen Unsicherheit liefert. Diese Dissertation bringt Fairness für verantwortungsvolles ML durch die Entwicklung von Ansätzen für die Lösung von praktischen Kernproblemen näher an die Anwendungen im Feld. Umfassende Experimente mit einer Vielzahl von synthetischen und realen Datensätzen zeigen, dass unsere Ansätze in der Praxis umsetzbar sind.The International Max Planck Research School for Computer Science (IMPRS-CS