A new dialectical theory of explanation

This paper offers a dialogue theory of explanation. A successful explanation is defined as a transfer of understanding in a dialogue system in which a questioner and a respondent take part. The questioner asks a special sort of why-question that asks for understanding of something and the respondent provides a reply that transfers understanding to the questioner. The theory is drawn from recent work on explanation in artificial intelligence (AI), especially in expert systems, but applies to scientific, legal and everyday conversational explanations.     

Enlarging the model of the human at the heart of human-centered AI: A social self-determination model of AI system impact

Increasing awareness of the harms that artificial intelligence (AI) systems can cause has inspired a movement towards creating more human-centered AI (HCAI). One way in which AI systems can be made more human-centered is by focusing on the effects they have on people's needs. However, existing theories of technology impact in HCAI drawn from human-computer interaction (HCI) and related fields such as psychology are not able to account for the ways in which both needs and the impact of technology on these are dynamically shaped by the social context. To address this limitation, in this paper we outline a Social Self-Determination Model (SSDM) of AI system impact. SSDM proposes that people's needs for self-determination can be individual or collective in a particular context depending on features of the person and their environment. Accordingly, because AI systems treat people differently depending on the groups they belong to, they can make needs for individual or collective self-determination psychologically relevant and either fulfill or hamper these. SSDM can be used to help designers and developers create more human-centered AI systems by quantifying the effects of these systems and the social environment in which they are embedded on people's fundamental psychological needs and wellbeing.     

(E)-Trust and Its Function: Why We Shouldn't Apply Trust and Trustworthiness to Human–AI Relations

With an increasing use of artificial intelligence (AI) systems, theorists have analyzed and argued for the promotion of trust in AI and trustworthy AI. Critics have objected that AI does not have the characteristics to be an appropriate subject for trust. However, this argumentation is open to counterarguments. Firstly, rejecting trust in AI denies the trust attitudes that some people experience. Secondly, we can trust other non-human entities, such as animals and institutions, so why can we not trust AI systems? Finally, human–AI trust is criticized based on a conception of human–human trust, which does not recognize the distinctiveness of the human–AI relationship. This article aims to refute these counterarguments based on the genealogical analyses of ‘trust’ and ‘trustworthiness’ of Karen Jones and Thomas Simpson, who show that trust and trustworthiness help to overcome vulnerabilities. This function of trust gives reason to use human–human trust as a standard. For this function, it is important that trustees are responsive to trust. While animals and institutions could be responsive, narrow AI systems are unable to be responsive to trust. Therefore, we should not apply trust to AI and instead direct our trust to those who can be responsive to and held responsible for our trust.     

Beyond the Belmont Principles: A Community‐Based Approach to Developing an Indigenous Ethics Model and Curriculum for Training Health Researchers Working with American Indian and Alaska Native Communities

Individuals responsible for carrying out research within their diverse communities experience a critical need for research ethics training materials that align with community values. To improve the capacity to meet local human subject protections, we created the research Ethics Training for Health in Indigenous Communities (rETHICS), a training curriculum aligned within American Indian and Alaska Native (AI/AN) context, culture, and community‐level ethical values and principles. Beginning with the Belmont Report and the Common Rule that defines research with human subjects (46 CFR 45), the authors convened three different expert panels (N = 37) to identify Indigenous research values and principles common across tribal communities. The resulting culturally grounded curriculum was then tested with 48 AI/AN individuals, 39 who also had recorded debriefing interviews. Using a thematic analysis, we coded the qualitative feedback from the expert panel discussions and the participant debriefings to assess content validity. Participants identified five foundational constructs needed to ensure cultural‐grounding of the AI/AN‐specific research training curriculum. These included ensuring that the module was: (a) framed within an AI/AN historical context; (b) reflected Indigenous moral values; (c) specifically linked AI/AN cultural considerations to ethical procedures; (d) contributed to a growing Indigenous ethics; and (e) provided Indigenous‐based ethics tools for decision making. Using community‐based consultation and feedback from participants led to a culturally grounded training curriculum that teaches research ethical principles and procedures for conducting research with AI/ANs. The curriculum is available for free and the community‐based process used can be adapted for other cultural groups.     

The logicist manifesto: At long last let logic-based artificial intelligence become a field unto itself

This paper is a sustained argument for the view that logic-based AI should become a self-contained field, entirely divorced from paradigms that are currently still included under the AI “umbrella”—paradigms such as connectionism and the continuous systems approach. The paper includes a self-contained summary of logic-based AI, as well as rebuttals to a number of objections that will inevitably be brought against the declaration of independence herein expressed.     

The role of the psychologist expert witness: Provider of perspective and input

The definition and role of the expert witness is reviewed. Two conceptual systems for viewing the world, the scientific method and the adversarial system, are compared. The reconciliation and integration of these two discrepant systems are discussed in terms of input versus outcome. The rules of evidence, as well as problems of communication between the systems, are reviewed with the aim of providing expert witnesses with a better understanding of effective testimony. Finally, collaboration between the two systems is called for in order to provide guidelines for both neuropsychologists and attorneys.     

Exploring the Theory and Paradigm Base for Wraparound

Until recently, family-centered policy and practice used expert models which defined families of children with serious emotional disturbance as dysfunctional A collaborative model, called wraparound, is emerging which engages these families as decision making participants, using naturally occurring strengths to wrap individualized supports around the child and family. However, because wraparound has been defined only through value-based principles, the fidelity of the model is threatened by a developmental paradox. Those who have received training and whose careers have been shaped in more traditional expert models of deficit remediation can interpret these value-based principles as an emergent form of case management methodology. Critical and constructivist paradigms, and ecological systems theory, form a basis for negotiating this paradox to maintain fidelity of wraparound process. Anchored in this base, and derived from wraparound's value-based principles, a single construct with an operative focus is suggested to ensure the integrity of this collaborative model.     

Legal aspects of decision-enhancing technologies

As advances in computer-based systems (CBS) have continued, development problems can occur which can adversely affect systems developers and users of these technologies, especially those designed to improve or affect decision making. One major difficulty concerns the legal liability that can occur as a result of defective or flawed systems development efforts. This article discusses several types of decision-enhancing technologies and how problems associated with the adequacy of testing of system design and appropriateness of warnings and directions/instructions dealing with CBS could adversely affect CBS developers from a legal perspective. Several propositions and suggested research perspectives are also presented. He received his Ph.D. in computer information systems from Arizona State University. His research interests include the impact on organizations and individuals of information systems and the potential legal liability to systems developers brought about by defective computer-based systems. His research has appeared inMIS Quarterly, Information & Management, AI Expert, AI & Society, Educational andPsychological Measurement andInformation Resources Management Journal. Kathleen Mykytyn is a consultant and research specializing in the impact of information systems and technologies on individuals and organizations. Her research has been published inMIS Quarterly, AI & Society, AI Expert, andInformation & Management.     

The role of cognitive architectures in general artificial intelligence

The term “Cognitive Architectures” indicates both abstract models of cognition, in natural and artificial agents, and the software instantiations of such models which are then employed in the field of Artificial Intelligence (AI). The main role of Cognitive Architectures in AI is that one of enabling the realization of artificial systems able to exhibit intelligent behavior in a general setting through a detailed analogy with the constitutive and developmental functioning and mechanisms underlying human cognition. We provide a brief overview of the status quo and the potential role that Cognitive Architectures may serve in the fields of Computational Cognitive Science and Artificial Intelligence (AI) research.     

Artificial Intelligence and Medical Humanities

The use of artificial intelligence in healthcare has led to debates about the role of human clinicians in the increasingly technological contexts of medicine. Some researchers have argued that AI will augment the capacities of physicians and increase their availability to provide empathy and other uniquely human forms of care to their patients. The human vulnerabilities experienced in the healthcare context raise the stakes of new technologies such as AI, and the human dimensions of AI in healthcare have particular significance for research in the humanities. This article explains four key areas of concern relating to AI and the role that medical/health humanities research can play in addressing them: definition and regulation of “medical” versus “health” data and apps; social determinants of health; narrative medicine; and technological mediation of care. Issues include data privacy and trust, flawed datasets and algorithmic bias, racial discrimination, and the rhetoric of humanism and disability. Through a discussion of potential humanities contributions to these emerging intersections with AI, this article will suggest future scholarly directions for the field.

     

Impacts of artificial intelligence on organizational decision making

Of all of the new technologies emerging in the late 20th century, the production of artificial intelligence may provide the most profound impacts on organizational decision making. Because the development of artificial intelligence technologies and models has largely been based on psychological models of human cognition, the effects of their implementation in complex social settings have not been thoroughly examined. This paper is an attempt to generate research which will develop a comprehensive understanding of the impacts of artificial intelligence and its role in complex organizations. A set of 11 hypotheses has been developed which examine the relationships between artificial intelligence technologies and the dimensions of organizational decision making. It is argued here that the implementation of expert systems will lead to less complex and political decision processes, while the implementation of natural language systems will lead to more complex and political decision processes.     

Building human-like communicative intelligence: A grounded perspective

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, “grounded”, perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building “grounded” linguistic intelligence. These components include embodying machines in a perception–action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world.     

What is it like to trust a rock? A functionalist perspective on trust and trustworthiness in artificial intelligence

The trustworthiness (or otherwise) of AI has been much in discussion of late, not least because of the recent publication of the EU Guidelines for Trustworthy AI. Discussions range from how we might make people trust AI to AI being not possible to trust, with many points inbetween. In this article, we question whether or not these discussions somewhat miss the point, which is that people are going ahead and basically doing their own thing anyway, and that we should probably help them. Acknowledging that trust is a heuristic that is widely used by humans in a range of situations, we lean on the literature concerning how humans make trust decisions, to arrive at a general model of how people might consider trust in AI (and other artefacts) for specific purposes in a human world. We then use a series of thought experiments and observations of trust and trustworthiness, to illustrate the use of the model in taking a functionalist perspective on trust decisions, including with machines. Our hope is that this forms a useful basis upon which to develop intelligent systems in a way that considers how and when people may trust them, and in doing so empowers people to make better trust decisions about AI.     

National Ethics Advisory Bodies in the Emerging Landscape of Responsible Research and Innovation

The article examines the role played by policy advice institutions in the governance of ethically controversial new and emerging science and technology in Europe. The empirical analysis, which aims to help close a gap in the literature, focuses on the evolution, role and functioning of national ethics advisory bodies (EABs) in Europe. EABs are expert bodies whose remit is to issue recommendations regarding ethical aspects of new and emerging science and technology. Negative experiences with the impacts of science and technology in the past have resulted in calls for increased transparency and broader participation and pluralism in expert advice and policy decision-making. Do national EABs function as inclusive, anticipatory “hybrid forums”? Or do they resemble more “classical” expert-oriented bodies, inspired by technocratic or decisionist approaches? As part of the empirical analysis of the role and functioning of institutional ethical advisory structures in 32 European countries, an extensive analysis of EAB websites and the content of publicly available documents on such institutions has been carried out, supplemented by an online survey of representatives of the EABs. One major finding of the empirical analysis is the very uneven distribution of “hybrid forum” features of EABs across Europe.     

Expert systems: A cognitive science perspective

The theory and technology of knowledge-based systems are intrinsically interdisciplinary and are closely related to the formalisms of cognitive psychology. In this paper, strategies of incorporating intelligence into a computer program are described along with a common architecture for expert systems, including choices of representation and inferential methods. The history of the field is traced from its origins in metamathematics and Newell and Simon’s (1961) GENERAL PROBLEM SOLVER to the Stanford Heuristic Programming Project that produced DENDRAL and MYCIN (Buchanan & Shortliffe, 1984). MYCIN gave rise to EMYCIN and a shell technology that has radically reduced the development time and cost of expert systems. Methodology and concepts are illustrated by transactions with a shell developed for graduate education and a demonstration knowledge base for the diagnosis of senile dementia. Knowledge-based systems and conventional programs are compared with respect to formalisms employed, applications, program characteristics, procedures supplied by the development environment, consistency, certainty, flexibility, and programmer’s viewpoint. The technology raises basic questions for cognitive psychology concerning knowledge and expertise.     

Domain‐based perceptions of risk: a case study of lay and technical community attitudes toward managed aquifer recharge

Despite growing water scarcity, communities in many parts of the developed world often reject technically and economically sound options for water augmentation. This paper reports findings from a study investigating risk perceptions associated with a proposed managed aquifer recharge scheme in Australia. Q Methodology was used to compare decision‐making frameworks of lay community and “technical expert” participants. Technical expert participants were also asked to approximate the decision‐making framework of a “typical” community member. The emerging contrasts between lay community frameworks and those approximated by technical experts suggest that there are prevailing yet errant assumptions about lay community attitudes toward new technologies. The findings challenge the characterization of the lay community and technical experts as being in entrenched opposition with one another.     

Using an AI creativity system to explore how aesthetic experiences are processed along the brain’s perceptual neural pathways

With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be used to explore the nature of the dual-pathway neuro-architecture of the human visual system and how this relates to higher cognitive judgments such as aesthetic experiences that rely on these divergent information streams. We propose a theoretical framework for how the movement within percepts such as video clips, causes the engagement of reflexive attention and a subsequent focus on visual information that are primarily processed via the dorsal stream, thereby modulating aesthetic experiences that rely on information relayed via the ventral stream. We outline our recent study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences.     

Investigation of Perceptual-Motor Behavior Across the Expert Athlete to Disabled Patient Skill Continuum can Advance Theory and Practical Application

A framework is presented of how theoretical predictions can be tested across the expert athlete to disabled patient skill continuum. Common-coding theory is used as the exemplar to discuss sensory and motor system contributions to perceptual-motor behavior. Behavioral and neural studies investigating expert athletes and patients recovering from cerebral stroke are reviewed. They provide evidence of bi-directional contributions of visual and motor systems to perceptual-motor behavior. Majority of this research is focused on perceptual-motor performance or learning, with less on transfer. The field is ripe for research designed to test theoretical predictions across the expert athlete to disabled patient skill continuum. Our view has implications for theory and practice in sports science, physical education, and rehabilitation.