Toward a sociology of machine learning explainability: Human–machine interaction in deep neural  network-based automated trading

Borch, Christian; Min, Bo Hee

doi:10.1177/20539517221111361

Cited by 24 publications

(11 citation statements)

References 53 publications

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“…The introduction of data-driven algorithms in the domain of medical imaging is one of the leading areas of technological development. A distinguishing characteristic of new algorithms is their black-box character: the complexity of understanding the relations between inputs and outputs 1 . Especially in the medical context, this has major implications for the development and deployment of these algorithms since medical decisions are high stakes and carry strict legal liabilities 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination

Mehrizi

Mol

Peter

et al. 2023

Sci Rep

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Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists’ diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the influence of (incorrect) algorithmic suggestions.

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Section: Introductionmentioning

confidence: 99%

The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination

Mehrizi

Mol

Peter

et al. 2023

Sci Rep

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“…Additionally, some machine learning models, such as deep neural networks, are complex and challenging to interpret. The lack of interpretability can be a concern in critical decision-making processes [90]. Radar data accuracy can be affected by the terrain and urban environments, leading to errors in flood mapping.…”

Section: Discussionmentioning

confidence: 99%

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Hitouri,

Mohajane,

Lahsaini

et al. 2024

Remote Sensing

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Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.

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“…Examples of such in-depth, immersive studies in AI-mediated workplaces have been published in this journal and elsewhere (e.g. Borch and Min, 2022; Borg, 2021); such designs should be extended to examine skill requirements.…”

Section: Research Agendamentioning

confidence: 99%

“…Whilst the literature has recognised the importance of skills as a key factor in the development and adoption of AI, there has been paucity of empirical research on the nature of skill requirements in AI-mediated workplaces. Recent publications, including in this journal, have highlighted the skill gaps that arise when humans interact with AI, for example, when workers seek to make sense of AI-generated predictions (Borch and Min, 2022) or when they validate AI's decisions without understanding the basis of the decisions (Anthony, 2021). The lack of understanding of skill requirements, particularly the scarcity of micro-level data such as AI-focused skill taxonomies, has been recognised as a key barrier to advancing our understanding and measurement of the impact of AI on the future of work more broadly (Frank et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and skills in the workplace: An integrative research agenda

Margaryan

2023

Big Data & Society

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The development and diffusion of artificial intelligence (AI) technologies in workplaces are transforming the nature of work practices and their constituent skill requirements. This dual transformation is challenging for workers, organisations and societies, who are faced with the need to develop and enhance extant and new skills required to succeed in increasingly AI-mediated work settings. Although literature has recognised skills as a key factor in the development and uptake of AI technologies, there has been paucity of empirical research on the precise nature of skill requirements in AI-mediated workplaces. This commentary argues that to advance our understanding of skill requirements in AI-mediated workplaces, an integrative, multidisciplinary, multimethod and multistakeholder approach is required. The commentary proposes an agenda for future research in this societally important but poorly understood area.

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Toward a sociology of machine learning explainability: Human–machine interaction in deep neural network-based automated trading

Cited by 24 publications

References 53 publications

The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination

The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Artificial intelligence and skills in the workplace: An integrative research agenda

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