6,194 research outputs found
AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing
Recently, many AI researchers and practitioners have embarked on research
visions that involve doing AI for "Good". This is part of a general drive
towards infusing AI research and practice with ethical thinking. One frequent
theme in current ethical guidelines is the requirement that AI be good for all,
or: contribute to the Common Good. But what is the Common Good, and is it
enough to want to be good? Via four lead questions, I will illustrate
challenges and pitfalls when determining, from an AI point of view, what the
Common Good is and how it can be enhanced by AI. The questions are: What is the
problem / What is a problem?, Who defines the problem?, What is the role of
knowledge?, and What are important side effects and dynamics? The illustration
will use an example from the domain of "AI for Social Good", more specifically
"Data Science for Social Good". Even if the importance of these questions may
be known at an abstract level, they do not get asked sufficiently in practice,
as shown by an exploratory study of 99 contributions to recent conferences in
the field. Turning these challenges and pitfalls into a positive
recommendation, as a conclusion I will draw on another characteristic of
computer-science thinking and practice to make these impediments visible and
attenuate them: "attacks" as a method for improving design. This results in the
proposal of ethics pen-testing as a method for helping AI designs to better
contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on
27-10-201
Impossibility Results in AI: A Survey
An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. In this paper, we have categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, tradeoffs, and intractability. We found that certain theorems are too specific or have implicit assumptions that limit application. Also, we added a new result (theorem) about the unfairness of explainability, the first explainability-related result in the induction category. We concluded that deductive impossibilities deny 100%-guarantees for security. In the end, we give some ideas that hold potential in explainability, controllability, value alignment, ethics, and group decision-making. They can be deepened by further investigation
Artificial virtuous agents in a multiâagent tragedy of the commons
Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents (AMAs), it has been proven difficult to approach from a computational perspective. In this work, we present the first technical implementation of artificial virtuous agents (AVAs) in moral simulations. First, we review previous conceptual and technical work in artificial virtue ethics and describe a functionalistic path to AVAs based on dispositional virtues, bottom-up learning, and top-down eudaimonic reward. We then provide the details of a technical implementation in a moral simulation based on a tragedy of the commons scenario. The experimental results show how the AVAs learn to tackle cooperation problems while exhibiting core features of their theoretical counterpart, including moral character, dispositional virtues, learning from experience, and the pursuit of eudaimonia. Ultimately, we argue that virtue ethics provides a compelling path toward morally excellent machines and that our work provides an important starting point for such endeavors
An Evaluation of GPT-4 on the ETHICS Dataset
This report summarizes a short study of the performance of GPT-4 on the
ETHICS dataset. The ETHICS dataset consists of five sub-datasets covering
different fields of ethics: Justice, Deontology, Virtue Ethics, Utilitarianism,
and Commonsense Ethics. The moral judgments were curated so as to have a high
degree of agreement with the aim of representing shared human values rather
than moral dilemmas. GPT-4's performance is much better than that of previous
models and suggests that learning to work with common human values is not the
hard problem for AI ethics.Comment: 8 page
A Computational-Hermeneutic Approach for Conceptual Explicitation
We present a computer-supported approach for the logical analysis and
conceptual explicitation of argumentative discourse. Computational hermeneutics
harnesses recent progresses in automated reasoning for higher-order logics and
aims at formalizing natural-language argumentative discourse using flexible
combinations of expressive non-classical logics. In doing so, it allows us to
render explicit the tacit conceptualizations implicit in argumentative
discursive practices. Our approach operates on networks of structured arguments
and is iterative and two-layered. At one layer we search for logically correct
formalizations for each of the individual arguments. At the next layer we
select among those correct formalizations the ones which honor the argument's
dialectic role, i.e. attacking or supporting other arguments as intended. We
operate at these two layers in parallel and continuously rate sentences'
formalizations by using, primarily, inferential adequacy criteria. An
interpretive, logical theory will thus gradually evolve. This theory is
composed of meaning postulates serving as explications for concepts playing a
role in the analyzed arguments. Such a recursive, iterative approach to
interpretation does justice to the inherent circularity of understanding: the
whole is understood compositionally on the basis of its parts, while each part
is understood only in the context of the whole (hermeneutic circle). We
summarily discuss previous work on exemplary applications of human-in-the-loop
computational hermeneutics in metaphysical discourse. We also discuss some of
the main challenges involved in fully-automating our approach. By sketching
some design ideas and reviewing relevant technologies, we argue for the
technological feasibility of a highly-automated computational hermeneutics.Comment: 29 pages, 9 figures, to appear in A. Nepomuceno, L. Magnani, F.
Salguero, C. Bar\'es, M. Fontaine (eds.), Model-Based Reasoning in Science
and Technology. Inferential Models for Logic, Language, Cognition and
Computation, Series "Sapere", Springe
Commitment or Control? Human Resource Management Practices in Female and Male-Led Businesses
This paper investigates the commitment-orientation of HRM practices in female- and male-led firms. A distinction is made between emphasizing commitment or control in the design of HRM practices. To test for gender differences use is made of a sample of 555 Dutch firms. Contrary to what is generally believed it is found that ĂąâŹâ when controlled for relevant factors related to the business (e.g., firm size, age, sector) ĂąâŹâ HRM in female-led firms is more control-oriented than that in male-led firms. More specifically, female-led firms are more likely to be characterized by fixed and clearly defined tasks, centralized decision-making and direct supervision of the production process.entrepreneurship;gender;human resource management;commitment;control
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