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Other literature type . 2025
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2025
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2025
License: CC BY
Data sources: Datacite
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Managing risk and building trustworthy AI solutions: Lessons from FTI Consulting

Authors: Abdelqader, Haneen; Baker, Nathalie; Calvino, Claudio; Korres, Dimitris; Pascale, Ignacio; Gillespie, Stuart; Sharan, Malvika; +2 Authors

Managing risk and building trustworthy AI solutions: Lessons from FTI Consulting

Abstract

As generative AI (GenAI) systems become more sophisticated, more reliable and more accessible, there is an increasing imperative for organisations to make the most of this technology. FTI Consulting, a global firm providing expertise for organisations facing crises and transformation, is no exception. With more than 8,000 employees located in 31 countries, FTI helps its clients manage change, mitigate risk and resolve disputes in areas ranging from financial, legal and regulatory to reputational and operational. FTI’s data science and analytics team is its hub of innovation, leading the company’s drive towards AI adoption, for the benefit of its staff and its clients. Team members have been part of the 2024 cohort of The Turing Way Practitioners Hub, acting as Experts in Residence (EiRs) on behalf of the company. In this case study, they explore how issues of ethics and responsibility are necessary components of technological advancement. Acknowledgements This case study is published under The Turing Way Practitioners Hub 2024-25 Cohort - case study series. The Practitioners Hub is The Turing Way project that works with experts from partnering organisations to promote data science best practices. In 2024, The Turing Way team welcomed FTI Consulting team members, Claudio Calvino, Nathalie Baker, Haneen Abdelqader, Ignacio Pascale and Dimitris Korres, as Experts in Residence to represent interests and opportunities to discuss, adopt and share their implementation approaches to data science and AI in FTI and related sectors. We thank them for leading the development of this case study. This work is supported by Innovate UK BridgeAI. The Practitioners Hub has also received funding and support from the Ecosystem Leadership Award under the EPSRC Grant EP/X03870X/1 & The Alan Turing Institute. The Turing Way Practitioners Hub’s 2024-25 Cohort was co-delivered by Dr Malvika Sharan, Senior Researcher - Open Research and Arielle Bennett, Senior Researcher - Open Source Practices. Stuart Gillespie is the technical writer for this case study and others in the series. Léllé Demertzi is the Research Project Manager. The Turing Way Practitioners Hub, designed and launched in 2023 by Dr Sharan, aims to accelerate the adoption of best practices. Through a six-month cohort-based program, the Hub facilitates knowledge sharing, skill exchange, case study co-creation, and the adoption of open science practices. It also fosters a network of 'Experts in Residence' across partnering organisations. For any comments, questions or collaboration with The Turing Way, please email: turingway@turing.ac.uk.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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