During this presentation and panel chaired by Clare Elford, the CEO of Clue Software, the use cases for generative AI are discussed. The panel includes:
- Siobhan Myles – Director of product
- Natasha Laird – General Counsel
- Mal Earnest – Director of R&D
- Matt Horne – Director of Intelligence and Investigations
They cover topics such as prioritizing AI implementation, ensuring data quality, and maintaining the integrity of evidence.
The panel emphasize the importance of responsible AI and the need for guardrails and frameworks to guide AI development. They also highlight the potential of generative AI in improving efficiency and data insights in intelligence and investigation processes. The conversation concludes with a call for collaboration and data interoperability in the industry.
Takeaways
- Generative AI has numerous use cases in intelligence and investigation processes, including automatic prioritization, ranking referrals, and identifying entities in text.
- Maintaining the integrity of evidence is crucial, and responsible AI practices, such as following a code of conduct and adhering to existing legislation, are essential.
- Data quality and data interoperability are foundational for successful implementation of generative AI.
- Collaboration and co-creation with industry experts and the sector are important for developing effective generative AI capabilities.
- Generative AI has the potential to significantly improve efficiency and provide valuable data insights in intelligence and investigation work.
Sound Bites
- “Generative AI can deliver efficiency and data insights for data-heavy investigations.”
- “Triage, large amounts of data, asking questions of data in natural language.”
- “Guardrails in place to ensure the integrity of evidence.”
Chapters
00:00 – Introduction to Clue Software and the Topic of AI in Evidence Integrity
02:41 – Use Cases for Generative AI in Intelligence and Investigation Processes
05:02 – Prioritizing AI Implementation and Responsible AI Practices
09:30 – Ensuring Data Quality for Successful Generative AI
13:09 – Foundations for Generative AI: Data Quality and Knowledge Graphs
15:12 – Key Takeaways: Maintaining Evidence Integrity and Collaboration
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