There is a blind spot in AI research

Crawford, Kate; Calo, Ryan

doi:10.1038/538311a

Cited by 341 publications

(171 citation statements)

References 3 publications

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“…Finally, a social-systems analysis is currently missing from research on conversational agents, an absence that has also been reported for artificial intelligence applications in previous literature 59 . There are currently no agreed methods to assess the long-term effects of this technology on human populations.…”

Section: Discussionmentioning

confidence: 99%

Conversational agents in healthcare: a systematic review

Laranjo

Dunn

Tong

et al. 2018

Journal of the American Medical Informatics Association

848

580

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ObjectiveOur objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.MethodsWe searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen’s kappa measured inter-coder agreement.ResultsThe database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies.ConclusionsThe use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting.Protocol RegistrationThe protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.

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Section: Discussionmentioning

confidence: 99%

Conversational agents in healthcare: a systematic review

Laranjo

Dunn

Tong

et al. 2018

Journal of the American Medical Informatics Association

848

580

View full text Add to dashboard Cite

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“…Underlying Max's inference was his assumption that people with bad credit scores do not earn such salaries. Director of data science Martin and DeepNetwork's CTO Justin 19 , however, believed otherwise. Arguing that the relationship between credit score and salary was tenuous at best, they told Max that his interpretation was incorrect.…”

Section: Max (Data Scientist)mentioning

confidence: 99%

Trust in Data Science

Passi

Jackson

2018

Proc. ACM Hum.-Comput. Interact.

134

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The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.

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“…Fears that AI might jeopardize jobs for human workers 7 , be misused by malevolent actors 8 , elude accountability or inadvertently disseminate bias and thereby undermine fairness 9 have been at the forefront of the recent scientific literature and media coverage. Several studies have discussed the topic of ethical AI [10][11][12][13] , notably in metaassessments [14][15][16] or in relation to systemic risks 17,18 and unintended negative consequences like algorithmic bias or discrimination [19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

The global landscape of AI ethics guidelines

2019

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In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guidelinedevelopment efforts with substantive ethical analysis and adequate implementation strategies. Table 1-Ethical guidelines for AI by country of issuer

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There is a blind spot in AI research

Cited by 341 publications

References 3 publications

Conversational agents in healthcare: a systematic review

Conversational agents in healthcare: a systematic review

Trust in Data Science

The global landscape of AI ethics guidelines

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