Would Archimedes Shout “Eureka” If He Had Google? Innovating with Search Algorithms

In this paper we investigate the relationship between algorithmic search tools and the innovation process. Today, search algorithms are used for all tasks, yet we know little about their impact on the well-studied innovation process. We suggest a theoretical framework based on centripetal and centrifugal forces that conceptualizes the relationship between the algorithmic design logics of search tools and the innovation process. We use it to illustrate the current challenges with the use of informational search tools based on design principles of popularity and personalization, for innovation. We propose the need to develop and use exploratory search models and tools for innovation

To engage or not to engage with AI for critical judgments : how professionals deal with opacity when using AI for medical diagnosis

rtificial intelligence (AI) technologies promise to transform how professionals conduct knowledge work by augmenting their capabilities for making professional judgments. We know little, however, about how human-AI augmentation takes place in practice. Yet, gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. We conducted an in-depth field study in a major U.S. hospital where AI tools were used in three departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three) did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices—practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call unengaged “augmentation.” Our study unpacks the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and contributes to literature on AI adoption in knowledge work

Intelligence Augmentation: Human Factors in AI and Future of Work

The availability of parallel and distributed processing at a reasonable cost and the diversity of data sources have contributed to advanced developments in artificial intelligence (AI). These developments in the AI computing environment are not concomitant with changes in the social, legal, and political environment. While considering deploying AI, the deployment context and the end goal of human intelligence augmentation for that specific context have surfaced as significant factors for professionals, organizations, and society. In this research commentary, we highlight some important socio-technical aspects associated with recent growth in AI systems. We elaborate on the intricacies of human-machine interaction that form the foundation of augmented intelligence. We also highlight the ethical considerations that relate to these interactions and explain how augmented intelligence can play a key role in shaping the future of human work

The Open Innovation in Science research field: a collaborative conceptualisation approach

Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We link dispersed knowledge on Open Innovation, Open Science, and related concepts such as Responsible Research and Innovation by proposing a unifying Open Innovation in Science (OIS) Research Framework. This framework captures the antecedents, contingencies, and consequences of open and collaborative practices along the entire process of generating and disseminating scientific insights and translating them into innovation. Moreover, it elucidates individual-, team-, organisation-, field-, and society‐level factors shaping OIS practices. To conceptualise the framework, we employed a collaborative approach involving 47 scholars from multiple disciplines, highlighting both tensions and commonalities between existing approaches. The OIS Research Framework thus serves as a basis for future research, informs policy discussions, and provides guidance to scientists and practitioners

Motivations and Solution Appropriateness in Crowdsourcing Challenges for Innovation

Crowdsourcing challenges are fast emerging as an effective tool for solving complex innovation problems. The main strength of the crowdsourcing model is that it brings together a large number of diverse people from all over the world to focus on solving a problem. This openness, however, results in a large number of solutions that are not appropriate, and this inhibits organizations from leveraging the value of crowdsourcing efficiently and effectively. It is therefore essential to identify ways to increase the appropriateness of solutions generated in a crowdsourcing challenge. This paper takes a step towards that by exploring what motivates the crowd to participate in these challenges and how these motivations relate to solution appropriateness. Drawing on data from InnoCentive, one of the largest crowdsourcing platforms for innovation problems, this paper shows that the various types of motivation driving crowd members to participate were related in different ways to the appropriateness of the solutions generated. In particular, intrinsic and extrinsic motivation were positively related to appropriateness whereas for learning and prosocial motivation the relationship was negative. The association between social motivation and appropriateness was not significant. The results have important implications for how to better design crowdsourcing challenges

How maker tools can accelerate ideation

Innovators face tremendous time pressures today, whether they are tackling urgent issues such as public health and climate change or designing new products to stay ahead in a fast-moving competitive market. To meet the challenge, companies are investing in a number of technologies that accelerate innovation, but for many, the process is still frustratingly slow. What should organizations do? Our short answer: Do not use accelerating technologies only for rapid prototyping; use them much earlier and differently, for rapid ideating

The no.1 question to ask when evaluating AI tools

In the fast-moving and highly competitive artificial intelligence sector, developers’ claims that their AI tools can make critical predictions with a high degree of accuracy are key to selling prospective customers on their value. Because it can be daunting for people who are not AI experts to evaluate these tools, leaders may be tempted to rely on the high-level performance metrics published in sales materials. But doing so often leads to disappointing or even risky implementations