Predictive coding, also known as technology-assisted review (TAR), is a machine learning-based method for ediscovery that involves using software to analyze and categorize large volumes of electronic data in order to identify documents that are relevant to a particular legal matter. While predictive coding has become an increasingly popular tool in the ediscovery process, there are several issues that can arise that can impact its effectiveness. Here are some of the main issues and some suggestions on how to address them:
- Bias in the training data: One of the most significant issues with predictive coding is the risk of bias in the training data used to teach the algorithm. If the algorithm is trained on a biased sample, it may not accurately identify relevant documents, leading to incorrect or incomplete results. To address this issue, it is essential to carefully select the training data, including both positive and negative examples, and to ensure that it is representative of the full range of documents in the dataset.
- Lack of transparency: Another issue with predictive coding is the lack of transparency in how the algorithm arrives at its results. This can make it difficult for attorneys to understand how the algorithm is making decisions and to verify its accuracy. To address this issue, it is important to use an algorithm that provides clear and detailed explanations of its decision-making process, as well as tools to validate its results.
- Inadequate quality control: Predictive coding algorithms require ongoing quality control to ensure that they are working effectively and accurately identifying relevant documents. Without proper quality control measures, there is a risk of errors and inaccuracies. To address this issue, it is essential to implement robust quality control processes, including regular sampling and validation of results, and ongoing monitoring and adjustment of the algorithm.
- Cost and complexity: Predictive coding can be expensive and complex, requiring significant investment in software, hardware, and expertise. This can make it difficult for small and mid-sized law firms to adopt this technology. To address this issue, it is important to carefully evaluate the costs and benefits of predictive coding and to consider alternative solutions, such as outsourcing ediscovery services to a third-party provider.
- Ethical considerations: Finally, predictive coding raises ethical considerations around the use of machine learning algorithms to make decisions that can have a significant impact on individuals' lives. To address this issue, it is important to ensure that the use of predictive coding is consistent with ethical principles and guidelines, including transparency, fairness, and accountability.
In summary, while predictive coding can be a powerful tool for ediscovery, it is important to be aware of the potential issues and to take steps to address them, including careful selection of training data, transparency and validation of results, robust quality control processes, and consideration of ethical principles and guidelines.
