AI: Demystifying AI: A World of Possibilities
Artificial Intelligence encompasses a vast array of technologies that aim to replicate human intelligence within computer systems. Subfields like Machine Learning (ML) utilize past data to make predictions, while robotics focuses on hardware aspects. Other approaches, such as neural networks and evolutionary computation, mimic natural behaviors and evolution. These AI subfields create interactions akin to human intelligence, customized to meet diverse user needs.
Understanding the eDiscovery Process
Electronic discovery (eDiscovery) involves identifying, collecting, and preparing data in a forensically sound manner for specific legal cases or investigations. Beyond emails and documents, eDiscovery encompasses instant messages, images, audio and video files, website content, mobile data, and entire databases. After proper data identification and collection, specialized software prepares it for review, where relevant information is extracted.
AI's Role in eDiscovery
AI offers an impressive range of tools to streamline the eDiscovery process, particularly during the review phase. By combining AI with traditional tools, we revolutionize eDiscovery, saving time and costs while improving accuracy. Here are some practical examples of AI in action:
Multilingual Document Review: Imagine facing a review with half the documents in Korean. AI-driven translation mechanisms enable seamless content interpretation without the need for human linguists, accelerating the review process.
Predictive Coding and Continuous Active Learning (CAL): Through machine learning models trained on a subset of manually reviewed documents, AI automatically classifies and prioritizes the remaining documents, flagging potentially relevant ones for further review. This technique can dramatically speed up review times and reduce expenses.
Automated Data Collection and Preservation: AI identifies and gathers electronically stored information (ESI) from diverse sources, such as emails, documents, databases, and social media. This automated process ensures the preservation of potentially crucial evidence, expediting investigations and enhancing overall efficiency.
Efficient Searching with OCR: AI-driven Optical Character Recognition (OCR) transforms data from scans or images into searchable text, enabling faster and more comprehensive searches.
Natural Language Processing and GenAI (NLP, GPT): AI leverages NLP techniques to understand and interpret human language, performing tasks such as entity recognition, sentiment analysis, and language translation, thereby enhancing the review process.
AI Empowering Managed Review
Managed review involves the meticulous analysis of vast document volumes in legal, regulatory, investigative, or due diligence contexts. Traditionally, human reviewers manually sift through documents to identify relevant information, confidential data, privileged documents, or specific criteria. With AI, including machine learning and natural language processing, managed review enters a new era of unprecedented possibilities:
Faster and Higher Quality Review: AI-powered automated solutions replace traditional manual processes, completing tasks faster and reducing human errors.
Unleashing AI Potential: By combining human expertise with AI capabilities, managed review becomes highly efficient and effective. Continuous human oversight ensures accuracy and addresses unforeseen challenges.
The future of eDiscovery and Managed Review lies in embracing AI's potential responsibly and ethically. While AI streamlines processes and improves efficiency, human expertise remains irreplaceable.
Stay up to date with the latest news
Subscribe for a newsletter
Contact us
Gaweł Gajdzis
Anna Stefaniuk-Kemona
