TAI-PRM

Trustworthy AI - Project Risk Management framework

Authors:

Eduardo Vyhmeister, Phd. Gabriel González-Castañé, Phd.

Description:

This work provides a framework for the management of risks blended with trustworthy AI within the manufacturing sector, TAI-PRM. It builds upon the Failure Mode and Effect Analysis method (FMEA) and the Industrial ISO 31000. The approach is based on the principle that ethical considerations must be handled as hazards. If these considerations are not adequately managed, they are expected to impact different sustainable pillars severely.

The current work presents a framework for project management of risks that focuses on adding trustworthy requirements to manage AI artefacts in the manufacturing sector, TAI-PRM. Existing risk management methods used on industrial processes are extended with the requirements of the trustworthy AI guidelines to comply with the present (and flexible to incorporate future) regulatory conditions on AI. The design objectives of the TAI-PRM are:

• To support management units and developers in incorporating trustworthy requirements within the AI life-cycle process. It is necessary to distinguish between legal and certification requirements and the values needed from a societal dimension.
• To secure the use of AI artefacts independently of legal and technical changes. The legislation heterogeneity applied in different countries on the use of AI can be varied; therefore, flexibility is key.
• To ease the combination of the framework proposed with other approaches that handle risk management. It needs to be designed as a complementary asset to these and not a replacement to facilitate its adoption.
• To facilitate an iterative process to handle risks on AI artefacts within the framework. Many processes in software do not follow a sequential development but spiral/ iterative development processes. Therefore, the framework must flexibly adapt to any development cycle applied by developers for its incorporation.
• To ensure that Key Performance Indicators (KPIs) can be tracked through well-defined metrics that register the progress on the ethical-based risks. Tracking KPIs is essential for their daily operations and business units. In addition, managerial levels can use these indicators to understand the impact of incorporating ethical aspects.
• To construct an architecture that addresses persona responsibilities and channels. With this aim, the framework must foster communication between technical and non-technical stakeholders.
• When possible, to foster the reuse of outcomes in other research areas and market segments to avoid duplication of effort. This is translated as savings on revenue and research time on future developments. In addition, well-structured risk identification can avoid the repetition of failing conditions on AI components with similar target objectives.
• To enable a seamless path for the transition to Industry 5.0. By linking ethical considerations and risks, the industry can handle trustworthy AI requirements as a Risk Management Process (RMP).

Publications:

1. Vyhmeister, E., Castane, G., Östberg, P. O., & Thevenin, S. (2022). A responsible AI framework: pipeline contextualisation. AI and Ethics, 1-23.
   Link to the paper on the journal:https://doi.org/10.1007/s43681-022-00154-8
   The paper: (https://rdcu.be/cTQ9T) Reference it:
   @article{vyhmeister2022responsible,
     title={A responsible AI framework: pipeline contextualisation},
     author={Vyhmeister, Eduardo and Castane, Gabriel and {"O}stberg, P-O and Thevenin, Simon},
     journal={AI and Ethics},
     pages={1--23},
     year={2022},
     publisher={Springer}
   }

2. Vyhmeister, E., Gonzalez-Castane, G., & Östberg, P. O. (2022). Risk as a driver for AI framework development on manufacturing. AI and Ethics, 1-20. Link to the paper on the journal:https://doi.org/10.1007/s43681-022-00159-3
   The paper: (https://rdcu.be/cTQ9W)
   Reference it!:
   @article{vyhmeister2022risk,
     title={Risk as a driver for AI framework development on manufacturing},
     author={Vyhmeister, Eduardo and Gonzalez-Castane, Gabriel and {"O}stbergy, P-O},
     journal={AI and Ethics},
     pages={1--20},
     year={2022},
     publisher={Springer}
   }

Contacts:

eduardo.vyhmeister@insight-centre.org
gabriel.castane@insight-centre.org