Collaborating Authors

Artificial Intelligence, Machine Ethics And Crowdsourcing


Shouldn't we decide democratically about the ethics of self-driving cars, human perspectives on machine ethics to its implications on politics? Because of this reliance, we need the tools (not excluding IoT) in the field of artificial intelligence to be more ethical to make our devices more integrative and better able aid to continue in our growth. However we shouldn't program machines to consider only logic when making some decisions. The clear case is how voice process algorithms silently can listen to our conversations and make up a model, into buying us products. However unethical this is, people try.

An Approach to Computing Ethics

AITopics Original Links

To make ethics computable, we've adopted an approach to ethics that involves considering multiple prima facie duties in deciding how one should act in an ethical dilemma. We believe this approach is more likely to capture the complexities of ethical decision making than a single, absolute-duty ethical theory. However, it requires a decision procedure for determining the ethically correct action when the duties give conflicting advice. To solve this problem, we employ inductive-logic programming to enable a machine to abstract information from ethical experts' intuitions about particular ethical dilemmas, to create a decision principle. We've tested our method in the MedEthEx proof-of-concept system, using a type of ethical dilemma that involves 18 possible combinations of three prima facie duties.

On Automating the Doctrine of Double Effect Artificial Intelligence

The doctrine of double effect ($\mathcal{DDE}$) is a long-studied ethical principle that governs when actions that have both positive and negative effects are to be allowed. The goal in this paper is to automate $\mathcal{DDE}$. We briefly present $\mathcal{DDE}$, and use a first-order modal logic, the deontic cognitive event calculus, as our framework to formalize the doctrine. We present formalizations of increasingly stronger versions of the principle, including what is known as the doctrine of triple effect. We then use our framework to simulate successfully scenarios that have been used to test for the presence of the principle in human subjects. Our framework can be used in two different modes: One can use it to build $\mathcal{DDE}$-compliant autonomous systems from scratch, or one can use it to verify that a given AI system is $\mathcal{DDE}$-compliant, by applying a $\mathcal{DDE}$ layer on an existing system or model. For the latter mode, the underlying AI system can be built using any architecture (planners, deep neural networks, bayesian networks, knowledge-representation systems, or a hybrid); as long as the system exposes a few parameters in its model, such verification is possible. The role of the $\mathcal{DDE}$ layer here is akin to a (dynamic or static) software verifier that examines existing software modules. Finally, we end by presenting initial work on how one can apply our $\mathcal{DDE}$ layer to the STRIPS-style planning model, and to a modified POMDP model.This is preliminary work to illustrate the feasibility of the second mode, and we hope that our initial sketches can be useful for other researchers in incorporating DDE in their own frameworks.

Artificial Intelligence And Ethics: The Need For EQ Along With IQ - Express Computer


As Artificial Intelligence gradually seeps into more industries, its need for emotion quotient becomes more pronounced. Monitoring Artificial Intelligence by setting an ethical framework is the way forward.

Toward the Engineering of Virtuous Machines Artificial Intelligence

While various traditions under the 'virtue ethics' umbrella have been studied extensively and advocated by ethicists, it has not been clear that there exists a version of virtue ethics rigorous enough to be a target for machine ethics (which we take to include the engineering of an ethical sensibility in a machine or robot itself, not only the study of ethics in the humans who might create artificial agents). We begin to address this by presenting an embryonic formalization of a key part of any virtue-ethics theory: namely, the learning of virtue by a focus on exemplars of moral virtue. Our work is based in part on a computational formal logic previously used to formally model other ethical theories and principles therein, and to implement these models in artificial agents.