PRESS RELEASE
INSIGHT SFI CENTRE AND GENESYS TO SCALE UP BIAS DETECTION PARTNERSHIP IN GALWAY
Open research programme extended as EU AI Act comes into force
03 April 2024, Galway: Insight SFI Research Centre for Data Analytics in Galway is scaling up its research partnership with Genesys, a global cloud leader in AI-powered experience orchestration, building on their collaboration around bias detection techniques in machine learning models that began in 2021. Genesys has signed a Phase 3 extension to its contract, committing to working with the Insight SFI Research Centre until 31st June 2025.
The new project; ‘Exploring gaps in Large Language Models for Trustworthy AI’; is focused on building AI systems that are trustworthy and ethical, adhering to the key principles of transparency and accountability. Together, the research teams are working with standardisation bodies to influence global standards in establishing frameworks for explainability and fairness in AI models and datasets.
This project builds on the bias detection research work completed in Phases 1 and 2. Phase 3, which kicked off in January 2024, will explore gaps in Large Language Models (LLMs) including domain-specific translations for under-resourced languages, prompt-based fine-tuning approaches and domain injection approaches.
Genesys and Insight SFI Research Centre have taken an open approach to this programme, as this research can benefit the wider technology sector in numerous ways, including: improving transparency on data and models, increasing awareness of the AI decision risks, contributing towards industry standards, and importantly, guiding AI systems’ adherence to upcoming regulations such as the EU Artificial Intelligence Act, which will come into force in the coming months.
Professor Edward Curry, Director of the Insight SFI Research Centre at University of Galway, added: ‘Building trustworthy AI through the development of systems with explainable processes is a core activity at Insight, Ireland’s national research centre on data and AI. Genesys is a global leader in AI-powered customer and employee experiences and our research partnership comes at a critical time as we move to a more regulated AI space. The open innovation approach of this research is of particular value to both partners, as we seek to support the wider mission of building more accountable AI systems in Europe and beyond.’
Project co-lead Dr Ihsan Ullah commented: “Bias exists at various stages of a machine learning pipeline. LLMs are trained on gigantic amounts of data from the web, hence, necessitating an investigation into potential biases affecting their decisions. This project will delve into automation of detection of bias at various levels using explainable AI techniques and our proposed linked dataspace, model, and data card schema. It will help in bringing fairness and standardising the machine learning pipeline to be adopted by academia and industry across Ireland and the EU.”
Joe Smyth, SVP of R&D, Digital and AI at Genesys, said: “Since embarking on this project with the Insight SFI Research Centre for Data Analytics, we’ve made tremendous progress to advance bias detection. As we continue, our ultimate objective is to help organisations ensure the trustworthiness of the LLMs they are using to fuel their AI capabilities and to further apply LLMs to underserved languages, so all cultures can reap the benefits of this revolutionary technology.”
Project co-lead, Andy Donald said, “This represents an exciting opportunity for Insight to contribute to a more transparent and trustworthy AI ecosystem. As we move to a more data-centric global society, it is imperative that we lead the charge on this important work, and we’re excited to collaborate with Genesys on this journey.”
Dr John McCrae project co-lead, added: “In this project we will investigate techniques to reduce the cost of training large language models for new domains and languages, enabling Genesys to further expand the reach of its products”.
The project was shortlisted for the ITAG Excellence Awards in 2023.
For further information contact:
Louise Holden, FH Media Consulting Ltd
louise@fhmediaconsulting.com 00 353 87 2423985
NOTES FOR THE EDITOR
INSIGHT SFI RESEARCH CENTRE FOR DATA ANALYTICS
The Insight SFI Research Centre for Data Analytics is one of Europe’s largest data analytics research organisations, with over 450 researchers, more than 80 industry partners and €150+ million in funding. Its research spans Fundamentals of Data Science, Sensing and Actuation, Scaling Algorithms, Model Building, Multi-Modal Analysis, Data Engineering and Governance, Decision Making and Trustworthy AI. Insight is made up of four host institutions at DCU, University of Galway, UCC and UCD. Insight’s partner sites are Maynooth University, Tyndall, TCD and UL. www.insight-centre.org
GENESYS
Genesys empowers more than 8,000 organisations in over 100 countries to improve loyalty and business outcomes by creating the best experiences for customers and employees. Through Genesys Cloud, the #1 AI-powered experience orchestration platform, Genesys delivers the future of CX to organisations of all sizes so they can provide empathetic, personalised experience at scale. As the trusted, all-in-one platform born in the cloud, Genesys Cloud accelerates growth for organisations by enabling them to differentiate with the right customer experience at the right time, while driving stronger workforce engagement, efficiency and operational improvements.
PUBLICATIONS PHASES 1 & 2
Phases 1 and 2 of the Insight SFI Centre/Genesys research partnership focused on establishing a baseline literature review on bias detection in customer text interactions and to embed the bias detection into a defined model, data and dataspace card schema using linked data techniques.
Two joint publications emanated from Phases 1 and 2 of the research:
- Towards a Semantic Approach for Linked Dataspace, Model and Data Cards WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023
- Bias Detection with A Semantic Approach for Linked Model, Data, and Dataspace Cards Submitted to Journal of Data & Knowledge Engineering