Hardcover: 352 pagesYear: 2014Publisher: Oxford University PressISBN-13: 978-0199678112“The first ultraintelligent machine is the last invention that man need ever make, provided that the machine i...

Superintelligence: Paths, Dangers, Strategies

Review of Object-Oriented Ontology: A New Theory of Everything

The interdisciplinary field of explainable artificial intelligence (XAI) aims to foster human understanding of black-box machine learning models through explanation-generating methods. Although the social sciences suggest that explanation is a social and iterative process between an explainer and an explainee, explanation user interfaces and their user interactions have not been systematically explored in XAI research yet. Therefore, we review prior XAI research containing explanation user interfaces for ML-based intelligent systems and describe different concepts of interaction. Further, we present observed design principles for interactive explanation user interfaces. With our work, we inform designers of XAI systems about human-centric ways to tailor their explanation user interfaces to different target audiences and use cases.

Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces.

Recent advances in Artificial Intelligence (AI) suggest that AI applications could transform healthcare delivery in the Global South. However, as researchers and technology companies rush to develop AI applications that aid the health of marginalized communities, it is critical to consider the needs and perceptions of the community health workers (CHWs) who will have to integrate these AI applications into the essential healthcare services they provide to rural communities. We describe a qualitative study examining CHWs’ perceptions of an AI application for automated disease diagnosis. Drawing on data from 21 interviews with CHWs in rural India, we characterize (1) CHWs’ knowledge, perceptions, and understandings of AI; and (2) the benefits and challenges that CHWs anticipate as AI applications are integrated into their workflows, including their opinions on automation of their work, possible misdiagnosis and errors, data access and surveillance issues, security and privacy challenges, and questions concerning trust. We conclude by discussing the implications of our work for HCI and AI research in low-resource environments.

“It cannot do all of my work”: Community Health Worker Perceptions of AI-Enabled Mobile Health Applications in Rural India

HCI research has explored AI as a design material, suggesting that designers can envision AI’s design opportunities to improve UX. Recent research claimed that enterprise applications offer an opportunity for AI innovation at the user experience level. We conducted design workshops to explore the practices of experienced designers who work on cross-functional AI teams in the enterprise. We discussed how designers successfully work with and struggle with AI. Our findings revealed that designers can innovate at the system and service levels. We also discovered that making a case for an AI feature’s return on investment is a barrier for designers when they propose AI concepts and ideas. Our discussions produced novel insights on designers’ role on AI teams, and the boundary objects they used for collaborating with data scientists. We discuss the implications of these findings as opportunities for future research aiming to empower designers in working with data and AI.

/pdf/how-experienced-designers-of-enterprise-applications-engage-rq3o32d9.pdf

How Experienced Designers of Enterprise Applications Engage AI as a Design Material

Technology is a material, though we don’t often perceive it as something we can easily manipulate with sensuous knowledge. In particular, we don’t consider digital algorithms within Artificial-Intelligence (AI) as a material we can design with as we generally lack the intimate knowledge a carpenter has of the grain of wood and the chisel in hand. Despite this lack of understanding, designers are contributing to the rapid implementation of AI in diverse areas, having a profound effect on the lives of millions. If designers lack a material knowledge of AI, how are they to adequately consider the desirability of its use? How do we pierce the veil of something that is perceived as intangible, where the interplay between materials and forces are obscured? In this paper, we present a design approach that utilises philosophical lenses to help designers adopt a material perspective of AI aiming towards a more considered use.

/pdf/what-s-it-like-to-be-alexa-an-exploration-of-artificial-3cg476ubnm.pdf

What’s it like to be Alexa? An exploration of Artificial Intelligence as a Material for Design.

KEYWORDS: More-than-humandesign researchproductive oscillationintermediate knowledgeresearch through design 

Productive Oscillation as a strategy for doing more-than-human design research

Researchers and designers working in industrial sectors seeking to incorporate Artificial Intelligence (AI) technology, will be aware of the emerging International Organisation for AI Legibility (IOAIL). IOAIL was established to overcome the eruption of obscure AI technology. One of the primary goals of IOAIL is the development of a proficient certification body providing knowledge to users regarding the AI technology they are being exposed to. To this end IOAIL produced a system of standardised icons for attaching to products and systems to indicate both the presence of AI and to increase the legibility of that AI's attributes. Whilst the process of certification is voluntary it is becoming a mark of trust, enhancing the usability and acceptability of AI-infused products through improved legibility. In this paper we present our experience of seeking certification for a locally implemented AI security system, highlighting the issues generated for those seeking to adopt such certification.

/pdf/the-process-of-gaining-an-ai-legibility-mark-ednem0th7i.pdf

The Process of Gaining an AI Legibility Mark

Artificial Intelligence (AI) is becoming widespread. There are many benefits associated with AI, but it’s adoption brings challenges relating to fairness, bias, and transparency. Such issues are particularly hard to address because conventions that highlight when an AI is present, how it works, and the consequences of using are not yet established: AI has a legibility problem. Design-led research can play a key role in exploring this challenge. Applying Research through Design (RtD) this paper explores AI legibility in three ways: (1) explaining why it makes sense to address AI legibility with design; (2) the presentation of prototypical icons designed to enhance AI legibility; (3) experimenting with how the icons may be used in the context of signage relating to potential applications of AI. Via these three lenses the paper argues that design’s role in improving AI legibility is critical.

/pdf/signs-of-the-time-making-ai-legible-57b4lbtxyi.pdf

Signs of the Time: Making AI Legible

Towards a Master Narrative for Trust in Autonomous Systems: Trust as a Distributed Concern

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