26,274 research outputs found
Artificial morality: Making of the artificial moral agents
Abstract:
Artificial Morality is a new, emerging interdisciplinary field that centres
around the idea of creating artificial moral agents, or AMAs, by implementing moral
competence in artificial systems. AMAs are ought to be autonomous agents capable of
socially correct judgements and ethically functional behaviour. This request for moral
machines comes from the changes in everyday practice, where artificial systems are being
frequently used in a variety of situations from home help and elderly care purposes to
banking and court algorithms. It is therefore important to create reliable and responsible
machines based on the same ethical principles that society demands from people. New
challenges in creating such agents appear. There are philosophical questions about a
machine’s potential to be an agent, or mora
l agent, in the first place. Then comes the
problem of social acceptance of such machines, regardless of their theoretic agency
status. As a result of efforts to resolve this problem, there are insinuations of needed
additional psychological (emotional and cogn
itive) competence in cold moral machines.
What makes this endeavour of developing AMAs even harder is the complexity of the
technical, engineering aspect of their creation. Implementation approaches such as top-
down, bottom-up and hybrid approach aim to find the best way of developing fully
moral agents, but they encounter their own problems throughout this effort
An End-to-End Conversational Style Matching Agent
We present an end-to-end voice-based conversational agent that is able to
engage in naturalistic multi-turn dialogue and align with the interlocutor's
conversational style. The system uses a series of deep neural network
components for speech recognition, dialogue generation, prosodic analysis and
speech synthesis to generate language and prosodic expression with qualities
that match those of the user. We conducted a user study (N=30) in which
participants talked with the agent for 15 to 20 minutes, resulting in over 8
hours of natural interaction data. Users with high consideration conversational
styles reported the agent to be more trustworthy when it matched their
conversational style. Whereas, users with high involvement conversational
styles were indifferent. Finally, we provide design guidelines for multi-turn
dialogue interactions using conversational style adaptation
From Manifesta to Krypta: The Relevance of Categories for Trusting Others
In this paper we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (Manifesta), namely explicitly readable signals indicating internal features (Krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open Multi Agent Systems
Data centric trust evaluation and prediction framework for IOT
© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas
Human Values as the Basis for Sustainable Information System Design
Information systems (IS) play an increasing role for individual well-being [3], for the environment [4], and for society at large [5]. Considering sustainability in IS development is therefore becoming paramount. However, companies today associate sustainability with extra cost and burden on their operations. As a result, many view sustainability more as a demand and a challenge rather than an opportunity. In this article, we argue that companies should rethink this attitude, as both sustainability and a business model can be understood as deeply rooted in human values
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