Floridi’s Information Ethics as Macro-ethics and Info-computational Agent-Based Models

Contemporary medical diagnostics has a dynamic moral landscape, which includes a variety of agents, factors, and components. A significant part of this landscape is composed of information technologies that play a vital role in doctors’ decision-making. This paper focuses on the so-called Intelligent Decision-Support System that is widely implemented in the domain of contemporary medical diagnosis. The purpose of this article is twofold. First, I will show that the IDSS may be considered a moral agent in the practice of medicine today. To develop this idea I will introduce the approach to artificial agency provided by Luciano Floridi. Simultaneously, I will situate this approach in the context of contemporary discussions regarding the nature of artificial agency. It is argued here that the IDSS possesses a specific sort of agency, includes several agent features (e.g. autonomy, interactivity, adaptability), and hence, performs an autonomous behavior, which may have a substantial moral impact on the patient’s well-being. It follows that, through the technology of artificial neural networks combined with ‘deep learning’ mechanisms, the IDSS tool achieves a specific sort of independence (autonomy) and may possess a certain type of moral agency. Second, I will provide a conceptual framework for the ethical evaluation of the moral impact that the IDSS may have on the doctor’s decision-making and, consequently, on the patient’s wellbeing. This framework is the Object-Oriented Model of Moral Action developed by Luciano Floridi. Although this model appears in many contemporary discussions in the field of information and computer ethics, it has not yet been applied to the medical domain. This paper addresses this gap and seeks to reveal the hidden potentialities of the OOP model for the field of medical diagnosis.

Due to the intensely increasing competition and rapidly changing demand, Mass Customization(MC) manufacture has been the new trend for modern enterprises to respond quickly to the variational and individual demand in global market. To satisfy the operation of MC manufacture, its supply chain must be effective and flexible enough. Findings of researches and practices have shown that some intelligent activities, such as negotiation, decision and collaboration, have become the “bottleneck” to improve the performance of supply chain. This paper applied the Autonomous Intelligent System(AIS) to deal with the intelligent activities intelligently and automatically, and thereof provided a new method for the Supply Chain Management(SCM) on MC manufacture. First, a new supply chain model based on E-HUB was presented according to the requirements of MC manufacture, and then the structure and operation of AIS were designed to support that SCM. Finally, the development technology of AIS was discussed.

The essential difficulty about Computer Ethics‘ (CE) philosophical status is a methodological problem: standard ethical theories cannot easily be adapted to deal with CE-problems, which appear to strain their conceptual resources, and CE requires a conceptual foundation as an ethical theory. Information Ethics (IE), the philosophical foundational counterpart of CE, can be seen as a particular case of ’’environmental‘‘ ethics or ethics of the infosphere. What is good for an information entity and the infosphere in general? This is the ethical question asked by IE. The answer is provided by a minimalist theory of deseerts: IE argues that there is something more elementary and fundamental than life and pain, namely being, understood as information, and entropy, and that any information entity is to be recognised as the centre of a minimal moral claim, which deserves recognition and should help to regulate the implementation of any information process involving it. IE can provide a valuable perspective from which to approach, with insight and adequate discernment, not only moral problems in CE, but also the whole range of conceptual and moral phenomena that form the ethical discourse.

Introduction: We discuss the modern ways of developing intelligent problem solvers, focusing on their shortcomings in terms of the efficiency of their application for planning purposeful behavior of autonomous mobile intelligent systems in a priori undescribed conditions of a problem environment. Purpose: Developing a model of knowledge representation and processing which would provide the ways to organize purposeful activity of autonomous intelligent mobile systems in uncertain environment. Methods: Synthesis of frame-like behavior scenarios in the form of polyvariable conditionally dependent predicates whose structure includes complex variables as well as related variables of types “object”, “event” and “relationship”; synthesis of heuristic rules for knowledge representation in the process of purposeful behavior planning. In order to represent complex variables in polyvariable conditionally dependent predicates, fuzzy semantic networks are used which can represent knowledge of variously purposed intelligent systems without regard to particular knowledge domains, being adaptable to a priori undescribed operational conditions. Results: We have proposed a structure of various polyvariable conditionally dependent predicates. On their base, an autonomous intelligent mobile system can organize various activities in a priori undescribed and unstable problem environments. Specially developed knowledge processing tools allow such a system to automatically plan its purposeful behavior in a space of subtasks during the fulfilment of tasks formulated for it. Practical relevance: The obtained results can be efficiently used in building intelligent problem solvers for autonomous intelligent mobile systems of various purpose, capable of performing complex tasks in a priori undescribed operational conditions.

Artificial intelligence (AI) plays an important role in current discussions on information and communication technologies (ICT) and new modes of algorithmic governance. It is an unavoidable dimension of what social mediations and modes of reproduction of our information societies will be in the future. While several works in artificial intelligence ethics (AIE) address ethical issues specific to certain areas of expertise, these ethical reflections often remain confined to narrow areas of application, without considering the global ethical issues in which they are embedded. We, therefore, propose to clarify the main approaches to AIE, their philosophical assumptions and the specific characteristics of each one of them, to identify the most promising approach to develop an ethical reflection on the deployment of AI in our societies, which is the one based on information ethics as proposed by Luciano Floridi. We will identify the most important features of that approach to highlight areas that need further investigation.

Research in intelligent autonomous systems has historically been constrained by access to scalable high-performance computing. The challenges associated with scalability and tractability have significantly influenced analytical approaches and may have led to analysis in the lower dimensional spaces. Such reductions may overly simplify approaches to model and exploit redundancy in manners that human perception is able to in a bidirectional inferencing process. Evidence indicates that human perception involves different methods of classifying, indexing and associating information according to different spatio-temporal and saliency metrics. Recent surge in high performance computing with multicore processors and wide spread access to large disk-space at finer granularity, have triggered a renewed assessment of existing approaches. A survey of the techniques lead to a compelling need for efficient methods for capturing and exploiting “context” and “context-specific information” to swiftly change the behaviors of intelligent systems. The talk will highlight the evolution of signal processing, linguistic hierarchy, computing models where information fusion is considered, and draw some parallels, to make a case for a layered architecture for context-adaptive autonomous systems. Interactive access to petascale supercomputing has given a newer framework to incorporate context in such studies at scale that is required to perform complex intelligence tasks. The talk will highlight our current research in this direction including our vision of the future of intelligent autonomous systems with embedded high-performance computing with reach back compute power.

Integrating self-health awareness in autonomous systems

This essay responds to the machine question in the affirmative, arguing that artifacts, like robots, AI, and other autonomous systems, can no longer be legitimately excluded from moral consideration. The demonstration of this thesis proceeds in four parts or movements. The first and second parts approach the subject by investigating the two constitutive components of the ethical relationship—moral agency and patiency. In the process, they each demonstrate failure. This occurs not because the machine is somehow unable to achieve what is considered necessary and sufficient to be a moral agent or patient but because the characterization of agency and patiency already fail to accommodate others. The third and fourth parts respond to this problem by considering two recent alternatives—the all-encompassing ontocentric approach of Luciano Floridi’s information ethics and Emmanuel Levinas’s eccentric ethics of otherness. Both alternatives, despite considerable promise to reconfigure the scope of moral thinking by addressing previously excluded others, like the machine, also fail but for other reasons. Consequently, the essay concludes not by accommodating the alterity of the machine to the requirements of moral philosophy but by questioning the systemic limitations of moral reasoning, requiring not just an extension of rights to machines, but a thorough examination of the way moral standing has been configured in the first place.

Purpose This paper aims to enlighten stakeholders about critical success factors (CSFs) in developing intelligent autonomous systems (IASs) by integrating artificial intelligence (AI) with robotics. It suggests a prioritization hierarchy model for building sustainable ecosystem for developing IASs. Design/methodology/approach This paper is based on the existing literature and on the opinion of 15 experts. All the experts have minimum of eight years of experience in AI and related technologies. The CSF theory is used as a theoretical lens and total interpretative structure modelling (TISM) is used for the prioritization of CSFs. Findings Developing countries like India could leverage IASs and associated technologies for solving different societal problems. Policymakers need to develop basic policies regarding data collection, standardized hardware, skilled manpower, funding and start-up culture that can act as building blocks in undertaking sustainable ecosystem for developing IASs and implementing national AI strategy. Clear-cut regulations need to be in place for the proper functioning of the ecosystem. Any technology that can function properly in India has better chances of working at the global level considering the size of the population. Research limitations/implications This paper had all its experts from India only, and that makes the limitation of this paper, as there is a possibility that some of the factors identified may not hold same significance in other countries. Practical implications Stakeholders will understand the critical factors that are important in developing sustainable ecosystem for IASs and what should be the possible order of activities corresponding to each CSF. Originality/value The paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs. Further, eight significant factors, that is, emerging economy multinational enterprises (EMNEs), governance, utility, manpower, capital, software, data and hardware, have come up as the most important factors in integrating AI with robotics in India.

This paper extends forward thinking by information ethics and business education scholars to introduce a Privacy Curriculum Taxonomy (PCT) that repurposes business curricula around the emerging personal information privacy paradigm. The seminal challenge confronting business education leaders is to respond to the ontological paradigm shift from a physical society driven by material and monetary processes, towards a digital society driven by information supply and the growing demand for information privacy. The PCT is advanced as an initial framework for engaging business curriculum planners in the considerations required to repurpose existing disciplines around digital society information and privacy processes. After a current literature review, the PCT is developed using a foundational set of information assurance principles. The PCT is business discipline specific, to catalyze incubation and further development within and across functional areas.

This work is devoted to the development of a prototype of the basic platform of the autonomous intelligent robotics system (AIRS). The frame of the experimental basic platform was made, main units and aggregates location was determined, testing of mechanical units and aggregates was carried out. Ultrasonic and infrared rangefinders were used to determine the distance to the object. The calibration of the sensors was performed when interacting with objects made of different materials, at different angles of the surfaces of these objects, and under different envi-ronmental conditions. An electronic printed circuit board has been developed to connect various sensors to an au-tonomous mobile robotic platform. The overall dimensions of the platform are 780x650x550 mm. The small size of the mobile basic platform allows it to be used on small sites where the use of other machines is economically unprofitable.

The recent history of information theory and science shows a trend in emphasis from quantitative measures to qualitative characterizations. In parallel, aspects of information are being developed, for example by Pedro Marijuan, Wolfgang Hofkirchner and others that are extending the notion of qualitative, non-computational information in the biological and cognitive domain and include meaning and function. However, there is as yet no consensus on whether a single accepted definition or theory of the concept of information is possible, leading to many attempts to view it as a complex, a notion with varied meanings or a group of different entities. In my opinion, the difficulties in developing a unified theory of information (UTI) that would include its qualitative and quantitative aspects and their relation to meaning are a consequence of implicit or explicit reliance on the principles of standard, truth-functional bivalent or multivalent logics. In reality, information processes like those of time, change and human consciousness are contradictory: they are regular and irregular; consistent and inconsistent; continuous and discontinuous. Since the indicated logics cannot accept real contradictions, they have been incapable of describing the multiple but interrelated characteristics of information. The framework for the discussion of information in this paper will be the new extension of logic to real complex processes that I have made, Logic in Reality (LIR)[1], which is grounded in the dualities and self-dualities of quantum physics and cosmology. LIR provides, among other things, new interpretations of the most fundamental metaphysical questions present in all discussions of information at physical, biological and cognitive levels of reality including, especially, those of time, continuity vs. discontinuity, and change, both physical and epistemological. I show that it can constitute a novel and general approach to the non-binary properties of information, including meaning and value. These properties subsume the notion of semantic information as well-formed, meaningful and truthful data as proposed most recently by Luciano Floridi. LIR supports the concept of 'biotic' information of Stuart Kauffmann, Robert Logan and their colleagues and that of meaningful information developed by Christophe Menant. Logic in Reality does not pretend to the level of rigor of an experimental or mathematical theory. It is proposed as a methodology to assist in achieving a minimum scientific legitimacy for a qualitative theory of information. My hope is that by seeing information, meaning and knowledge as dynamic processes, evolving according to logical rules in my extended sense of logic, some of the on-going issues on the nature and function of information may be clarified. [1] Brenner, J. E. 2008. Logic in Reality. Dordrecht: Springer.

The recent history of information theory and science shows a trend in emphasis from quantitative measures to qualitative characterizations. In parallel, aspects of information are being developed, for example by Pedro Marijuan, Wolfgang Hofkirchner and others that are extending the notion of qualitative, non-computational information in the biological and cognitive domain to include meaning and function. However, there is as yet no consensus on whether a single acceptable definition or theory of the concept of information is possible, leading to many attempts to view it as a complex, a notion with varied meanings or a group of different entities. In my opinion, the difficulties in developing a Unified Theory of Information (UTI) that would include its qualitative and quantita-tive aspects and their relation to meaning are a consequence of implicit or explicit reliance on the principles of standard, truth-functional bivalent or multivalent logics. In reality, information processes, like those of time, change and human con-sciousness, are contradictory: they are regular and irregular; consistent and inconsistent; continuous and discontinuous. Since the indicated logics cannot accept real contradictions, they have been incapable of describing the multiple but interre-lated characteristics of information. The framework for the discussion of information in this paper will be the new extension of logic to real complex processes that I have made, Logic in Reality (LIR), which is grounded in the dualities and self-dualities of quantum physics and cos-mology. LIR provides, among other things, new interpretations of the most fundamental metaphysical questions present in discussions of information at physical, biological and cognitive levels of reality including, especially, those of time, continuity vs. discontinuity, and change, both physical and epistemological. I show that LIR can constitute a novel and general ap-proach to the non-binary properties of information, including meaning and value. These properties subsume the notion of semantic information as well-formed, meaningful and truthful data as proposed most recently by Luciano Floridi. LIR sup-ports the concept of ‘biotic’ information of Stuart Kauffmann, Robert Logan and their colleagues and that of meaningful information developed by Christophe Menant. Logic in Reality does not pretend to the level of rigor of an experimental or mathematical theory. It is proposed as a meth-odology to assist in achieving a minimum scientific legitimacy for a qualitative theory of information. My hope is that by seeing information, meaning and knowledge as dynamic processes, evolving according to logical rules in my extended sense of logic, some of the on-going issues on the nature and function of information may be clarified.

Many studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this article, we present a model of the generative aspects of episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing parts based on general semantic information in a process we call semantic completion. The model combines two neural network architectures known from machine learning, the vector-quantized variational autoencoder (VQ-VAE) and the pixel convolutional neural network (PixelCNN). As episodes, we use images of digits and fashion items (MNIST) augmented by different backgrounds representing context. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch, and it generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally, we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong. This model contributes to a deeper understanding of the interplay between episodic memory and semantic information in the generative process of recalling the past.

Future intelligent autonomous systems (IAS) are inevitably deciding on moral and legal questions, e.g. in self-driving cars, health care or human-machine collaboration. As decision processes in most modern sub-symbolic IAS are hidden, the simple political plea for transparency, accountability and governance falls short. A sound ecosystem of trust requires ways for IAS to autonomously justify their actions, that is, to learn giving and taking reasons for their decisions. Building on social reasoning models in moral psychology and legal philosophy such an idea of >>Reasonable Machines<< requires novel, hybrid reasoning tools, ethico-legal ontologies and associated argumentation technology. Enabling machines to normative communication creates trust and opens new dimensions of AI application and human-machine interaction. Keywords: Trusthworthy and Explainable AI, Ethico-Legal Governors, Social Reasoning Model, Pluralistic and Expressive Normative Reasoning