Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One (...) of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between-subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk-seeking behavior among participants. We developed cognitive and heuristics models using instance-based learning theory and natural mean heuristic. Results reveal models’ dependency on recent and frequent observations during information sampling. These experience-based cognitive models could help build artificial intelligence models with fewer preference reversals. (shrink)

Purpose Digital transformation leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes. Artificial intelligence plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need (...) to integrate ethics in AI led DT. The purpose of this study is to develop an “AI led ethical digital transformation framework”. Design/methodology/approach Based on the literature survey, various existing business ethics decision-making models were synthesised. The authors mapped essential characteristics such as intensity and the individual, organisational and opportunity factors of ethics models with the proposed AI led ethical DT. The DT framework is evaluated using a thematic analysis of 23 expert interviews with relevant AI ethics personas from industry and society. The qualitative data of the interviews and opinion data has been analysed using MAXQDA software. Findings The authors have explored how AI can drive the ethical DT framework and have identified the core constituents of developing an AI led ethical DT framework. Backed by established ethical theories, the paper presents how DT pillars are related and sequenced to ethical factors. This research provides the potential to examine theoretically sequenced ethical factors with practical DT pillars. Originality/value The study establishes deduced and induced ethical value codes based on thematic analysis to develop guidelines for the pursuit of ethical DT. The authors identify four unique induced themes, namely, corporate social responsibility, perceived value, standard benchmarking and learning willingness. The comprehensive findings of this research, supported by a robust theoretical background, have substantial implications for academic research and corporate applicability. The proposed AI led ethical DT framework is unique and can be used for integrated social, technological and economic ethical research. (shrink)

Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression of healthcare research. (...) Simply put, the application of data science to ethically guided healthcare research appears to be a dream come true. Hence, in this paper, we discuss the current practices, challenges, and limitations of the data collection process during medical image analysis (MIA) conducted as part of healthcare research and propose an ethical data collection framework to guide data scientists to address the possible ethical concerns before commencing data analytics over a medical dataset. (shrink)

Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values (...) such as transparency and fairness from the outset. Drawing on insights from psychological theories, we assert the need to understand the values that underlie decisions made by individuals involved in creating and deploying AI systems. We describe how this understanding can be leveraged to increase engagement with de-biasing and fairness-enhancing practices within the AI healthcare industry, ultimately leading to sustained behavioral change via autonomy-supportive communication strategies rooted in motivational and social psychology theories. In developing these pathways to engagement, we consider the norms and needs that govern the AI healthcare domain, and we evaluate incentives for maintaining the status quo against economic, legal, and social incentives for behavior change in line with transparency and fairness values. (shrink)

AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns that arise (...) with unintended AI influence. I argue that unintended AI influence has important implications for the way we perceive and evaluate human-AI interaction. To make this point approachable from both the theoretical and practical side, and to avoid anthropocentricallyladen ambiguities, I introduce the notion of decision points. Based on this, the main argument of this paper will be presented in two consecutive steps: i) unintended AI influence doesn’t allow for an appropriate determination of decision points-this will be introduced as decision-point-dilemma, and ii) this has important implications for the ascription of responsibility. (shrink)

Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed (...) consent process; and criteria for waivers of informed consent. In accordance with respect for persons and communities, investigators and IRBs should systematically consider potential group harm when designing and reviewing protocols, respectively. Research participants should be informed about any potential group harm in the consent process. We call for additional public discussion, empirical research, and normative analysis on these issues to determine the right regulatory and policy path forward. (shrink)

Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...) treated in a dignified and respectful way in various healthcare decision contexts. Participants were subject to a 2 (human or AI decision maker) x 2 (positive or negative decision outcome) x 2 (diagnostic or resource allocation healthcare scenario) factorial design. We found evidence of a “human bias” (i.e., a preference for human over AI decision makers) and an “outcome bias” (i.e., a preference for positive over negative outcomes). However, we found that for perceptions of respectful and dignified interpersonal treatment, it matters more who makes the decisions in diagnostic cases and it matters more what the outcomes are for resource allocation cases. We also found that humans were consistently viewed as appropriate decision makers and AI was viewed as dehumanizing, and that participants perceived they were treated better when subject to diagnostic as opposed to resource allocation decisions. Thematic coding of open-ended text responses supported these results. We also outline the theoretical and practical implications of these findings. (shrink)