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It is, therefore, safe to wonder if the legal industry will react similarly to MachineLearning systems, especially those specifically designed to address legal problems. world where justice will be mediated and delivered by AI, we should first understand how this MachineLearning product actually works.
Next, we plan to expand the product’s scope to cover more aspects of the litigation process, to improve the machinelearning summarization model, and to develop visualizations of evidence based on the data present in the chronology. Finally, we plan to build integrations with e-discovery and practice management products.
How we’re unique: Starting from basic marketplace, Amazon+Uber for lawyers, approach, AppearMe is implementing machinelearning to automate routine legal work, minimize errors and missed deadlines by targeting the $65B litigation support market and offering free case management tools (a $1.1B Demo video: [link]. Demo video: [link].
It is based on advanced machinelearning models that learn patterns from vast amounts of data and can produce novel outputs based on that learning. If AI assists in a legal decision that’s later challenged, how can I defend its use? reshape practices, making them more efficient and perhaps even more equitable.
It is based on advanced machinelearning models that learn patterns from vast amounts of data and can produce novel outputs based on that learning. If AI assists in a legal decision that’s later challenged, how can I defend its use? reshape practices, making them more efficient and perhaps even more equitable.
How we’re unique: Starting from basic marketplace, Amazon+Uber for lawyers, approach, AppearMe is implementing machinelearning to automate routine legal work, minimize errors and missed deadlines by targeting the $65B litigation support market and offering free case management tools (a $1.1B Demo video: [link]. Demo video: [link].
Next, we plan to expand the product’s scope to cover more aspects of the litigation process, to improve the machinelearning summarization model, and to develop visualizations of evidence based on the data present in the chronology. Finally, we plan to build integrations with e-discovery and practice management products.
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