According to Hal Brooks, CEO of HaystackID, “HaystackID Discovery Intelligence encapsulates everything we do. It manifests what we have developed all along – a holistic approach to solving discovery challenges with a combination of AI, data science, machine learning, and our next-generation review capabilities.”
According to the recent overview from HaystackID, by synergistically harnessing the potential of artificial intelligence, the precision of data science, the power of machine learning, and the practicality of expertly trained and managed reviewers, HaystackID Discovery Intelligence delivers insight and intelligence that allows you to reach decision points faster and more economically than previously possible.
A significant factor in the adoption of algorithmic systems for decision-making is their capacity to process large amounts of varied data sets (i.e. big data), which can be paired with machine learning methods in order to infer statistical models directly from the data. The same properties of scale, complexity, and autonomous model inference however are linked to increasing concerns that many of these systems are opaque to the people affected by their use and lack clear explanations for the decisions they make.
This report from the US GAO describes an accountability framework for artificial intelligence (AI). The framework is organized around four complementary principles and describes key practices for federal agencies and other entities that are considering and implementing AI systems. Each practice includes a set of questions for entities, auditors, and third-party assessors to consider, along with audit procedures and types of evidence for auditors and third-party assessors to collect.
Developed based on the European Union Agency for Cybersecurity (ENISA) framework for artificial intelligence lifecycle stages and modified through the lens of the Electronic Discovery Reference Model (EDRM), the HaystackID Cyber Discovery Framework defines, depicts, and discusses a strategic framework that may be useful for understanding and applying the discipline of data and legal discovery in support of cybersecurity-centric challenges.
Cyber Discovery can be defined as the application of a combination of data discovery and legal discovery approaches to enable the exploration of patterns, trends, and relationships within unstructured and structured data with the objective of uncovering insight and intelligence to proactively or reactively respond to cybersecurity-centric challenges. The presented definition and framework, based on high-level artificial intelligence lifecycle stages as developed by the European Union Agency for Cybersecurity (ENISA) and modified through the lens of traditional eDiscovery planning and practices grounded within the Electronic Discovery Reference Model (EDRM), represents one potential methodology for describing and framing the stages and tasks of cyber discovery.
Amazon Kendra is a highly accurate and easy to use enterprise search service that’s powered by machine learning. Kendra delivers powerful natural language search capabilities to websites and applications so end users can more easily find the information they need within the vast amount of content spread across their company.
Released for general availability by AWS, Amazon Textract is a fully managed service that uses machine learning to automatically extract text and data, including from tables and forms, in virtually any document without the need for manual review, custom code, or machine learning experience.
Machine learning has matured as a mathematical discipline and now joins the many subfields of mathematics that deal with the burden of unprovability and the unease that comes with it. Perhaps results such as this one will bring to the field of machine learning a healthy dose of humility, even as machine-learning algorithms continue to revolutionize the world around us.