Evidence map›Paper›PMID 35028559›Full record

ArticleFrontiers in artificial intelligence2021

Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey.

Branka Hadji Misheva, David Jaggi, Jan-Alexander Posth, Thomas Gramespacher, Joerg Osterrieder

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Branka Hadji MishevaZHAW, School of Engineering, Institute of Data Analysis and Process Design, Winterthur, Switzerland.
David JaggiZHAW, School of Management and Law, Department Banking and Finance, Winterthur, Switzerland.
Jan-Alexander PosthZHAW, School of Management and Law, Department Banking and Finance, Winterthur, Switzerland.
Thomas GramespacherZHAW, School of Management and Law, Department Banking and Finance, Winterthur, Switzerland.
Joerg OsterriederZHAW, School of Engineering, Institute of Data Analysis and Process Design, Winterthur, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is one of the most sought-after innovations in the financial industry. However, with its growing popularity, there also is the call for AI-based models to be understandable and transparent. However, understandably explaining the inner mechanism of the algorithms and their interpretation is entirely audience-dependent. The established literature fails to match the increasing number of explainable AI (XAI) methods with the different stakeholders' explainability needs. This study addresses this gap by exploring how various stakeholders within the Swiss financial industry view explainability in their respective contexts. Based on a series of interviews with practitioners within the financial industry, we provide an in-depth review and discussion of their view on the potential and limitation of current XAI techniques needed to address the different requirements for explanations.

Indexed as

artificial intelligenceexplainable AIfinancemachine learningresponsible AIrisk management

Identifiers

PMID35028559
PMCPMC8751385

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.