The State of AI Ethics Report (June 2020)

These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and development is geared towards researchers and practitioners alike who are making decisions on behalf of their organizations in considering the societal impacts of AI-enabled solutions. We cover a wide set of areas in this report spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over the past quarter surfacing signal from the noise so that you are equipped with the right tools and knowledge to confidently tread this complex yet consequential domain

Artificial intelligence and knowledge sharing: Contributing factors to organizational performance

The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society

Artificial intelligence and knowledge sharing: Contributing factors to organizational performance

The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society

Artificial intelligence and knowledge sharing: Contributing factors to organizational performance

The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society

Artificial intelligence and knowledge sharing: Contributing factors to organizational performance

The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society

Employee experience –the missing link for engaging employees: Insights from an MNE 's AI ‐based HR ecosystem

Abstract: Analyzing multiple data sources from a global information technology (IT) consulting multinational enterprise (MNE), this research unpacks the configuration of a digitalized HR ecosystem of artificial intelligence(AI)‐assisted human resource management (HRM) applications and HR platforms. This study develops a novel theoretical framework mapping the nature and purpose of a digitalized AI‐assisted HR ecosystem for delivering exceptional employee experience (EX), an antecedent to employee engagement (EE). Employing the theoretical lenses of EX, EE, AI‐mediated social exchange, and engagement platforms, this study's overarching aim of this article is to establish how AI‐assisted HRM fits into an organization's ecosystem and, second, how it impacts EX and EE. Our findings show that AI‐assisted applications for HRM enhance EX and, thus, EE. We also see increases in employee productivity and HR function's effectiveness. Implications for research and practice are also discussed

Networked Trust: Computational Understanding of Interpersonal Trust Online

Supplemental file(s) description: Dataset accompanying ICSWM '17 paper: A Computational Approach to Perceived Trustworthiness of Airbnb Host Profiles, Dataset accompanying CSCW '17 paper: Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles, Code accompanying CHI '19 paper: Why Do People Trust Their Social Groups?My doctoral research develops a deeper understanding of interpersonal trust online through computational methods, in the context of online exchange platforms including peer-to-peer marketplaces, sharing economy platforms, and social networks. Through analyzing images in product listings on eBay and LetGo.com, language in profiles on Airbnb, and networks in social groups on Facebook, I show how different algorithms help understand and predict interpersonal trust in each context. Findings reveal patterns of interpersonal trust. For example, high-quality images are perceived as more trustworthy than stock imagery; language of promises lead to higher perceived trustworthiness through conventional signaling; and smaller, denser, and more private social groups are trusted more. These findings inform the design of online exchange platforms. The algorithms predicting trust could also be used for better ranking and recommendation to “engineer” interpersonal trust. Going forward, I propose a lens of “networked trust” to view interpersonal trust online, which has three focuses: (1) cues in Computer-Mediated Communication; (2) embeddedness in social networks; and (3) increasing mediation by algorithms. The networked trust framework can be used to frame future trust research in other contexts, such as misinformation. Finally, two research agenda were charted by this dissertation — AI-Mediated Communication and AI-Mediated Exchange Theory, which future work can develop on