More-Than-Human Design and AI

arXiv (Cornell University), 2023

Explainable AI (XAI) aims to bridge the gap between complex algorithmic systems and human stakeholders. Current discourse often examines XAI in isolation as either a technological tool, user interface, or policy mechanism. This paper proposes a unifying ethical framework grounded in moral duties and the concept of reciprocity. We argue that XAI should be appreciated not merely as a right, but as part of our moral duties that helps sustain a reciprocal relationship between humans affected by AI systems. This is because, we argue, explanations help sustain constitutive symmetry and agency in AI-led decision-making processes. We then assess leading XAI communities and reveal gaps between the ideal of reciprocity and practical feasibility. Machine learning offers useful techniques but overlooks evaluation and adoption challenges. Human-computer interaction provides preliminary insights but oversimplifies organizational contexts. Policies espouse accountability but lack technical nuance. Synthesizing these views exposes barriers to implementable, ethical XAI. Still, positioning XAI as a moral duty transcends rights-based discourse to capture a more robust and complete moral picture. This paper provides an accessible, detailed analysis elucidating the moral value of explainability.

Information Systems Frontiers

A plethora of submissions to this Special Issue showed that while AI is increasingly being embedded in information systems with potentially severe consequences for humans, including medical diagnostics (McKinney et al., 2020), floorplan design (Khang, 2021), job recruitment (Dastin, 2018), art (Ramesh et al., 2022), credit scoring (Wang et al., 2019), and autonomous vehicles (Grigorescu et al., 2019), the social aspects of interactions between humans and AI systems remain under-researched. This is surprising given IS's rich tradition as a discipline in theorizing and studying both the technical elements as well as the human aspects in designing and managing complex systems (Beydoun et al., 2019; Dwivedi et al., 2015). Artificial Intelligence (AI), described as non-human intelligence that is flexible and autonomous enough to understand and learn from data to achieve specific outcomes (Kaplan & Haenlein, 2019), has reached-and even surpassed-the ability of humans in multiple domains (Arrieta et al., 2020;

The symposium “The Human Face of Artificial Intelligence: Infrastructures, Narratives, Ethics” that took place at the University of California, Irvine on October 17, 2019, brought together an interdisciplinary and international group of scholars to discuss new challenges and opportunities around the systems of AI. Broadly defined as intelligent automated systems that can analyze their environment, make decisions and adapt their behavior by learning from experience, systems of AI steadily permeate social and political spaces, fomenting novel conversations about law, ethics, governance, sociality, and humanity. If a complex AI system malfunctions and causes harm to humans, who or what should be found liable and according to which criteria? Should AI be viewed as a mere technological tool, or an autonomous agent with free will? How does artificial intelligence reshape our conversations about legal personhood and human spirituality within this increasingly complex intersection of humanity and technology? Cite: Bill Maurer and Daivi Rodima-Taylor, "In Search of the Human Face of Artificial Intelligence," Society for Social Studies of Science, Backchannels, Report-backs, November 12, 2019. https://members.4sonline.org/news_archive_headlines.php?org_id=4S&sniid=34424641#34424641

IEEE Technology and Society Magazine, 2023

IEEE transactions on technology and society, 2024

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Biases in Artifcial Intelligence (AI) systems or their results are one important issue that demands AI explainability. Despite the prevalence of AI applications, the general public are not necessarily equipped with the ability to understand how the black-box algorithms work and how to deal with biases. To inform designs for explainable AI (XAI), we conducted in-depth interviews with major stakeholders, both end-users (n = 24) and engineers (n = 15), to investigate how they made sense of AI applications and the associated biases according to situations of high and low stakes. We discussed users' perceptions and attributions about AI biases and their desired levels and types of explainability. We found that personal relevance and boundaries as well as the level of stake are two major dimensions for developing user trust especially during biased situations and informing XAI designs. CCS CONCEPTS • Human-centered computing → Empirical studies in collaborative and social computing; Empirical studies in HCI.

2020

We examine the problem of explainable AI (xAI) and explore what delivering xAI means in practice, particularly in contexts that involve formal or informal and ad-hoc collaboration where agency and accountability in decision-making are achieved and sustained interactionally. We use an example from an earlier study of collaborative decision-making in screening mammography and the difficulties users faced when trying to interpret the behavior of an AI tool to illustrate the challenges of delivering usable and effective xAI. We conclude by setting out a study programme for future research to help advance our understanding of xAI requirements for safe and ethical AI.

ArXiv, 2020

Algorithmic systems are increasingly deployed to make decisions in many areas of people's lives. The shift from human to algorithmic decision-making has been accompanied by concern about potentially opaque decisions that are not aligned with social values, as well as proposed remedies such as explainability. We present results of a qualitative study of algorithmic decision-making, comprised of five workshops conducted with a total of 60 participants in Finland, Germany, the United Kingdom, and the United States. We invited participants to reason about decision-making qualities such as explainability and accuracy in a variety of domains. Participants viewed AI as a decision-maker that follows rigid criteria and performs mechanical tasks well, but is largely incapable of subjective or morally complex judgments. We discuss participants' consideration of humanity in decision-making, and introduce the concept of 'negotiability,' the ability to go beyond formal criteria an...

IEEE Software, 2020

Online journal of communication and media technologies, 2023

As the global influence of artificial intelligence (AI) in our daily lives and the looming advent of artificial general intelligence (AGI) become increasingly apparent, the need for a sophisticated interpretive framework intensifies. This paper introduces 'AIsmosis'-a term that captures AI's gradual, nuanced integration into society, and akin to the biological process of osmosis. AI's integration dynamics are examined through the lens of three pivotal theories: social construction of technology, technological determinism, and diffusion of innovations. These theories collectively elucidate the sociocultural influences on AI, the potential repercussions of unchecked technological growth, and the factors driving the adoption of novel technologies. Building upon these explorations, the 'controlled AIsmosis' conceptual framework emerges, emphasizing ethically conscious development, active stakeholder communication, and democratic dialogue in the context of AI technology adoption. Rooted in communicative action theory, this framework illuminates AI's transformative impact on society. It calls for a comprehensive evaluation of systems that steer AI diffusion and their potential impacts, acknowledging the pervasive influence of AI and transcending traditional disciplinary boundaries. This work underscores the need for a multidisciplinary and interdisciplinary approach in investigating the complex AI-society interplay and understanding the ethical and societal consequences of AIsmosis.