2,660 research outputs found
A Computational Model of Commonsense Moral Decision Making
We introduce a new computational model of moral decision making, drawing on a
recent theory of commonsense moral learning via social dynamics. Our model
describes moral dilemmas as a utility function that computes trade-offs in
values over abstract moral dimensions, which provide interpretable parameter
values when implemented in machine-led ethical decision-making. Moreover,
characterizing the social structures of individuals and groups as a
hierarchical Bayesian model, we show that a useful description of an
individual's moral values - as well as a group's shared values - can be
inferred from a limited amount of observed data. Finally, we apply and evaluate
our approach to data from the Moral Machine, a web application that collects
human judgments on moral dilemmas involving autonomous vehicles
Ethics of Artificial Intelligence Demarcations
In this paper we present a set of key demarcations, particularly important
when discussing ethical and societal issues of current AI research and
applications. Properly distinguishing issues and concerns related to Artificial
General Intelligence and weak AI, between symbolic and connectionist AI, AI
methods, data and applications are prerequisites for an informed debate. Such
demarcations would not only facilitate much-needed discussions on ethics on
current AI technologies and research. In addition sufficiently establishing
such demarcations would also enhance knowledge-sharing and support rigor in
interdisciplinary research between technical and social sciences.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
Bayesian Inference of Social Norms as Shared Constraints on Behavior
People act upon their desires, but often, also act in adherence to implicit
social norms. How do people infer these unstated social norms from others'
behavior, especially in novel social contexts? We propose that laypeople have
intuitive theories of social norms as behavioral constraints shared across
different agents in the same social context. We formalize inference of norms
using a Bayesian Theory of Mind approach, and show that this computational
approach provides excellent predictions of how people infer norms in two
scenarios. Our results suggest that people separate the influence of norms and
individual desires on others' actions, and have implications for modelling
generalizations of hidden causes of behavior.Comment: 7 pages, 5 figures, to appear in CogSci 2019, code available at
https://github.com/ztangent/norms-cogsci1
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing
concerns about machines' morality or a lack thereof. Yet, teaching morality to
machines is a formidable task, as morality remains among the most intensely
debated questions in humanity, let alone for AI. Existing AI systems deployed
to millions of users, however, are already making decisions loaded with moral
implications, which poses a seemingly impossible challenge: teaching machines
moral sense, while humanity continues to grapple with it.
To explore this challenge, we introduce Delphi, an experimental framework
based on deep neural networks trained directly to reason about descriptive
ethical judgments, e.g., "helping a friend" is generally good, while "helping a
friend spread fake news" is not. Empirical results shed novel insights on the
promises and limits of machine ethics; Delphi demonstrates strong
generalization capabilities in the face of novel ethical situations, while
off-the-shelf neural network models exhibit markedly poor judgment including
unjust biases, confirming the need for explicitly teaching machines moral
sense.
Yet, Delphi is not perfect, exhibiting susceptibility to pervasive biases and
inconsistencies. Despite that, we demonstrate positive use cases of imperfect
Delphi, including using it as a component model within other imperfect AI
systems. Importantly, we interpret the operationalization of Delphi in light of
prominent ethical theories, which leads us to important future research
questions
Advancing Computational Models of Narrative
Report of a Workshop held at the Wylie Center, Beverly, MA, Oct 8-10 2009Sponsored by the AFOSR under MIT-MURI contract #FA9550-05-1-032
Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions
Artificial Intelligence (AI) has been used extensively in automatic decision
making in a broad variety of scenarios, ranging from credit ratings for loans
to recommendations of movies. Traditional design guidelines for AI models focus
essentially on accuracy maximization, but recent work has shown that
economically irrational and socially unacceptable scenarios of discrimination
and unfairness are likely to arise unless these issues are explicitly
addressed. This undesirable behavior has several possible sources, such as
biased datasets used for training that may not be detected in black-box models.
After pointing out connections between such bias of AI and the problem of
induction, we focus on Popper's contributions after Hume's, which offer a
logical theory of preferences. An AI model can be preferred over others on
purely rational grounds after one or more attempts at refutation based on
accuracy and fairness. Inspired by such epistemological principles, this paper
proposes a structured approach to mitigate discrimination and unfairness caused
by bias in AI systems. In the proposed computational framework, models are
selected and enhanced after attempts at refutation. To illustrate our
discussion, we focus on hiring decision scenarios where an AI system filters in
which job applicants should go to the interview phase
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
Human commonsense understanding of the physical and social world is organized
around intuitive theories. These theories support making causal and moral
judgments. When something bad happens, we naturally ask: who did what, and why?
A rich literature in cognitive science has studied people's causal and moral
intuitions. This work has revealed a number of factors that systematically
influence people's judgments, such as the violation of norms and whether the
harm is avoidable or inevitable. We collected a dataset of stories from 24
cognitive science papers and developed a system to annotate each story with the
factors they investigated. Using this dataset, we test whether large language
models (LLMs) make causal and moral judgments about text-based scenarios that
align with those of human participants. On the aggregate level, alignment has
improved with more recent LLMs. However, using statistical analyses, we find
that LLMs weigh the different factors quite differently from human
participants. These results show how curated, challenge datasets combined with
insights from cognitive science can help us go beyond comparisons based merely
on aggregate metrics: we uncover LLMs implicit tendencies and show to what
extent these align with human intuitions.Comment: 34 pages, 7 figures. NeurIPS 202
Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research
With language technology increasingly affecting individuals' lives, many
recent works have investigated the ethical aspects of NLP. Among other topics,
researchers focused on the notion of morality, investigating, for example,
which moral judgements language models make. However, there has been little to
no discussion of the terminology and the theories underpinning those efforts
and their implications. This lack is highly problematic, as it hides the works'
underlying assumptions and hinders a thorough and targeted scientific debate of
morality in NLP. In this work, we address this research gap by (a) providing an
overview of some important ethical concepts stemming from philosophy and (b)
systematically surveying the existing literature on moral NLP w.r.t. their
philosophical foundation, terminology, and data basis. For instance, we analyse
what ethical theory an approach is based on, how this decision is justified,
and what implications it entails. Our findings surveying 92 papers show that,
for instance, most papers neither provide a clear definition of the terms they
use nor adhere to definitions from philosophy. Finally, (c) we give three
recommendations for future research in the field. We hope our work will lead to
a more informed, careful, and sound discussion of morality in language
technology.Comment: to be published in EMNLP 2023 Finding
The Neuroscience of Moral Judgment: Empirical and Philosophical Developments
We chart how neuroscience and philosophy have together advanced our understanding of moral judgment with implications for when it goes well or poorly. The field initially focused on brain areas associated with reason versus emotion in the moral evaluations of sacrificial dilemmas. But new threads of research have studied a wider range of moral evaluations and how they relate to models of brain development and learning. By weaving these threads together, we are developing a better understanding of the neurobiology of moral judgment in adulthood and to some extent in childhood and adolescence. Combined with rigorous evidence from psychology and careful philosophical analysis, neuroscientific evidence can even help shed light on the extent of moral knowledge and on ways to promote healthy moral development
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