Uncertain Machine Ethical Decisions Using Hypothetical Retrospection
- URL: http://arxiv.org/abs/2305.01424v2
- Date: Wed, 12 Jul 2023 16:40:22 GMT
- Title: Uncertain Machine Ethical Decisions Using Hypothetical Retrospection
- Authors: Simon Kolker, Louise Dennis, Ramon Fraga Pereira, and Mengwei Xu
- Abstract summary: We propose the use of the hypothetical retrospection argumentation procedure, developed by Sven Ove Hansson.
Actions are represented with a branching set of potential outcomes, each with a state, utility, and either a numeric or poetic probability estimate.
We introduce a preliminary framework that seems to meet the varied requirements of a machine ethics system.
- Score: 8.064201367978066
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose the use of the hypothetical retrospection argumentation procedure,
developed by Sven Ove Hansson to improve existing approaches to machine ethical
reasoning by accounting for probability and uncertainty from a position of
Philosophy that resonates with humans. Actions are represented with a branching
set of potential outcomes, each with a state, utility, and either a numeric or
poetic probability estimate. Actions are chosen based on comparisons between
sets of arguments favouring actions from the perspective of their branches,
even those branches that led to an undesirable outcome. This use of arguments
allows a variety of philosophical theories for ethical reasoning to be used,
potentially in flexible combination with each other. We implement the
procedure, applying consequentialist and deontological ethical theories,
independently and concurrently, to an autonomous library system use case. We
introduce a preliminary framework that seems to meet the varied requirements of
a machine ethics system: versatility under multiple theories and a resonance
with humans that enables transparency and explainability.
Related papers
- Sequential Manipulation Against Rank Aggregation: Theory and Algorithm [119.57122943187086]
We leverage an online attack on the vulnerable data collection process.
From the game-theoretic perspective, the confrontation scenario is formulated as a distributionally robust game.
The proposed method manipulates the results of rank aggregation methods in a sequential manner.
arXiv Detail & Related papers (2024-07-02T03:31:21Z) - Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement [92.61557711360652]
Language models (LMs) often fall short on inductive reasoning, despite achieving impressive success on research benchmarks.
We conduct a systematic study of the inductive reasoning capabilities of LMs through iterative hypothesis refinement.
We reveal several discrepancies between the inductive reasoning processes of LMs and humans, shedding light on both the potentials and limitations of using LMs in inductive reasoning tasks.
arXiv Detail & Related papers (2023-10-12T17:51:10Z) - Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? [78.3738172874685]
Making moral judgments is an essential step toward developing ethical AI systems.
Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality.
This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research.
arXiv Detail & Related papers (2023-08-29T15:57:32Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Active Inference in Robotics and Artificial Agents: Survey and
Challenges [51.29077770446286]
We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning.
We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
arXiv Detail & Related papers (2021-12-03T12:10:26Z) - Have a break from making decisions, have a MARS: The Multi-valued Action
Reasoning System [0.0]
Multi-valued Action Reasoning System (MARS) is an automated value-based ethical decision-making model for artificial agents (AI)
Given a set of available actions and an underlying moral paradigm, by employing MARS one can identify the ethically preferred action.
arXiv Detail & Related papers (2021-09-07T18:44:24Z) - Landscape of Machine Implemented Ethics [0.20305676256390928]
This paper surveys the state-of-the-art in machine ethics, that is, considerations of how to implement ethical behaviour in robots, unmanned autonomous vehicles, or software systems.
The emphasis is on covering the breadth of ethical theories being considered by implementors, as well as the implementation techniques being used.
There is no consensus on which ethical theory is best suited for any particular domain, nor is there any agreement on which technique is best placed to implement a particular theory.
arXiv Detail & Related papers (2020-09-01T10:34:59Z) - Modeling Voting for System Combination in Machine Translation [92.09572642019145]
We propose an approach to modeling voting for system combination in machine translation.
Our approach combines the advantages of statistical and neural methods since it can not only analyze the relations between hypotheses but also allow for end-to-end training.
arXiv Detail & Related papers (2020-07-14T09:59:38Z) - Towards Contrastive Explanations for Comparing the Ethics of Plans [4.393037165265444]
We present how contrastive explanations can be used for comparing the ethics of plans.
We build upon an existing ethical framework to allow users to make suggestions to plans and receive contrastive explanations.
arXiv Detail & Related papers (2020-06-22T21:38:16Z) - From Probability to Consilience: How Explanatory Values Implement
Bayesian Reasoning [0.10152838128195464]
We propose a Bayesian account of how explanatory values fit together to guide explanation.
The resulting taxonomy provides a set of predictors for which explanations people prefer.
This framework also enables us to reinterpret the explanatory vices that drive conspiracy theories, delusions, and extremist ideologies.
arXiv Detail & Related papers (2020-06-03T16:11:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.