Teaching AI Ethics: Observations and Challenges

This report summarises the experience in teaching Artificial Intelligence (AI) Ethics as an elective masters level course at the University of Bergen. The goal of the summary is twofold: 1) to draw lessons for teaching this in-high demand very new discipline; 2) to serve as a basis in developing a bachelor level AI Ethics course for students of artificial intelligence. AI Ethics as a topic is particularly challenging to teach as the discipline itself is very new and no textbooks have been established. The added challenge is introducing methodologies and skills from humanity- and social sciences to students of computational and information sciences

Teaching AI Ethics: Observations and Challenges

This report summarises the experience in teaching Artificial Intelligence (AI) Ethics as an elective masters level course at the University of Bergen. The goal of the summary is twofold: 1) to draw lessons for teaching this in-high demand very new discipline; 2) to serve as a basis in developing a bachelor level AI Ethics course for students of artificial intelligence. AI Ethics as a topic is particularly challenging to teach as the discipline itself is very new and no textbooks have been established. The added challenge is introducing methodologies and skills from humanity- and social sciences to students of computational and information sciences.publishedVersio

Aggregation of probabilisitic logically related judgments

Information aggregation is at the core of many problems in computer science. Judgement aggregation models multi-agent decision making by aggregating individual opinions from various sources. It does however assume that the sources are certain in their opinions and also subject to the same logical constraints. We relax both of these assumptions and build a more general framework with uncertain information sources that we model in probabilistic logic. We also propose aggregation functions for this new framework

An Abstract Formal Basis for Digital Crowds

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behaviour of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.Comment: 32 pages, 4 figure

The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end users. All of these actors are stakeholders affected by the behavior of the autonomous system. We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of the autonomous system. We propose an ethical recommendation component, which we call Jiminy, that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. Jiminy represents the ethical views of each stakeholder by using normative systems, and has three ways of resolving moral dilemmas involving the opinions of the stakeholders. First, Jiminy considers how the arguments of the stakeholders relate to one another, which may already resolve the dilemma. Secondly, Jiminy combines the normative systems of the stakeholders such that the combined expertise of the stakeholders may resolve the dilemma. Thirdly, and only if these two other methods have failed, Jiminy uses context-sensitive rules to decide which of the stakeholders take preference. At the abstract level, these three methods are characterized by the addition of arguments, the addition of attacks among arguments, and the removal of attacks among arguments. We show how Jiminy can be used not only for ethical reasoning and collaborative decision making, but also for providing explanations about ethical behavior

Detecting bots with temporal logic

Social bots are computer programs that act like human users on social media platforms. Social bot detection is a rapidly growing field dominated by machine learning approaches. In this paper, we propose a complementary method to machine learning by exploring bot detection as a model checking problem. We introduce Temporal Network Logic (TNL) which we use to specify social networks where agents can post and follow each other. Using this logic, we formalize different types of social bot behavior with formulas that are satisfied in a model of a network with bots. We also consider an extension of the logic where we explore the expressive power of including elements from hybrid logic in our framework. We give model checking algorithms for TNL and its hybrid extension, and show that the complexity of the former is in P and the latter in PSPACE.publishedVersio

A partial taxonomy of judgment aggregation rules, and their properties

The literature on judgment aggregation is moving from studying impossibility results regarding aggregation rules towards studying specific judgment aggregation rules. Here we give a structured list of most rules that have been proposed and studied recently in the literature, together with various properties of such rules. We first focus on the majority-preservation property, which generalizes Condorcet-consistency, and identify which of the rules satisfy it. We study the inclusion relationships that hold between the rules. Finally, we consider two forms of unanimity, monotonicity, homogeneity, and reinforcement, and we identify which of the rules satisfy these properties

Automatic Detection of Manipulative Consent Management Platforms and the Journey into the Patterns of Darkness

We study how to automatically classify different types of manipulative interface design pattern for Content Management Platforms (CMPs), also known as a cookie consents. Our approach uses a scraper to extract different features of CMPs. We then classify the CMP, based on these features, into one of five patterns defined specifically for CMPs. We evaluate our automatic "detector" using four different statistical measures. We also consider factors that cause misclassifications and discuss how to potentially avoid them.publishedVersio

Egalitarian judgment aggregation

Egalitarian considerations play a central role in many areas of social choice theory. Applications of egalitarian principles range from ensuring everyone gets an equal share of a cake when deciding how to divide it, to guaranteeing balance with respect to gender or ethnicity in committee elections. Yet, the egalitarian approach has received little attention in judgment aggregation—a powerful framework for aggregating logically interconnected issues. We make the first steps towards filling that gap. We introduce axioms capturing two classical interpretations of egalitarianism in judgment aggregation and situate these within the context of existing axioms in the pertinent framework of belief merging. We then explore the relationship between these axioms and several notions of strategyproofness from social choice theory at large. Finally, a novel egalitarian judgment aggregation rule stems from our analysis; we present complexity results concerning both outcome determination and strategic manipulation for that rule.publishedVersio