AI can be a robust and valuable tool, but it is still prone to errors, and as the amount of AI-generated content grows, validation becomes essential. The best way to minimize errors and bias is to validate the source data before using it for machine learning.
Artificial intelligence is rapidly becoming an essential part of business operations. The challenge is, can you trust AI? Like all digital technology, the quality of AI is only as good as the information provided to train it – which may inadvertently fall into the undesirable category of “garbage in/garbage out.” That’s why it’s essential to validate AI-generated content.
The data used to train the large language models (LLMs) must be accurate to eliminate factual errors from creeping into AI models. Preconceptions, prejudice, and other biases are part of the real world. They will be reflected in AI data sources, such as social media, so content validation must also eliminate bias. Detecting falsified content is also essential when validating AI content to eliminate deepfakes.
Faulty algorithms can also introduce errors, including bias. For example, Amazon stopped using its AI hiring algorithm when it discovered it favored applicants with the words “executed” or “captured” on their resume, which favored male applicants. MIT researchers also found that facial analysis technologies tended to have higher error rates for minorities, especially minority women.
You must also be conscious of biased data sources that will skew generative AI models. Using a limited number of data sources can create bias. In our facial recognition example, the biased results can be eliminated by introducing more ethnic facial characteristics into the model, i.e., diversifying the data set.
Of course, there’s the other side of the coin, looking at Google’s infamous Gemini launch, which was inserting diversity into its images in a historically inaccurate way. While interpretations abound as to why this happened, the more objective takeaway is that arbitrarily inserted diversity principles as an attempt to counteract bias are not a good way to solve for biased data sources.
Inaccuracies and discriminatory biases tend to be baked into AI models at an early stage, which makes them hard to weed out. So, the best strategy is to validate the source data before using it to train LLMs and AI models.
See also: NIST: AI Bias Goes Way Beyond Data
Eliminating Bias in AI Models
Using real-world data sources to train AI may introduce biases based on gender, sexual orientation, race, income, and other factors. Once those biases are introduced, eliminating them requires an intimate understanding of data science, social forces, and data collection. And tainted AI models amplify the problem by deploying those biases at scale.
For example, ProPublica found that a criminal justice algorithm used in Broward County, Florida, mislabeled black defendants as “high risk” at twice the rate of white defendants. Research also found that AI models using news articles to train natural language processing models tend to exhibit gender stereotypes.
The best way to eliminate bias is to identify it in advance. There are three sources of bias:
Training data – AI systems base decisions on training data. Review data sampling for groups likely to be over- or underrepresented to identify training data bias. Be sure to use diversified training datasets to avoid bias as well. For example, do facial recognition algorithms favor Caucasians, or does security data create racial bias by including regional information about predominantly black neighborhoods?
Algorithms – Flawed training data proliferates when algorithms repeatedly generate errors or amplify bias. Programming errors can also contribute to bias, such as developers unfairly weighting decision-making factors. For example, using factors such as income or proficiency in English could discriminate against immigrants or minorities. On the other hand, using algorithms to attempt to reverse bias has its own pitfalls.
Cognitive bias – People build biases into AI models based on personal judgments about what datasets are used and how they are weighed. For example, cognitive bias could result if the model uses datasets gathered in the United States instead of worldwide.
Validating AI Datasets
Validating source data before using it to train LLMs is the best way to eliminate AI bias and inaccuracies. Source verification ensures that data is reliable and authoritative. Be sure to cross-check source information to verify its authenticity. Use standardized data collection methods to ensure consistency across datasets. Validate datasets to ensure accurate model training. Use cross-validation techniques to assess the AI models’ and data subsets’ performance and accuracy.
Dirty data can also skew AI modes. Be sure to normalize data before using it for AI training. Look for missing values and outliers. Automating data collection also minimizes human bias and errors.
Regular data audits can identify and correct any inaccuracies. Review data entry protocols and look for discrepancies as part of an audit. It also pays to perform manual reviews of processes to verify data accuracy and identify errors that automation may miss.
Validation datasets are invaluable for providing an unbiased assessment of training data and tuning model hyperparameters. Validation data are datasets used to provide an unbiased assessment of training data and to tune model hyperparameters.
As machine learning and statistical analytics became more sophisticated, validation data was developed to test the performance and capabilities of the training model, detecting wrong assumptions from faulty data and improving overall reliability. For example, validation data is used in financial cybersecurity to validate datasets for fraud. Validation data is also used with natural language processing to validate sentiment analysis and improve predictive analytics.
Validating Video and Images
Deepfakes have become a growing concern as AI-generated videos and images are increasingly used for disinformation and criminal activities. Visual content should be validated to rule out AI manipulation.
Deepfake detection requires training neural network models to look for signs of data manipulation, such as unnatural facial expressions or other less visible attributes. Deep learning models can automatically detect unusual patterns and features and be fine-tuned for specific tasks, but they require an inordinate amount of training data.
Many deepfake detection techniques look for anomalies in facial features, such as altering eyes, nose, and mouth. Convolutional Neural Networks (CNNs) use single images or capture frames from video images, and Recurrent Neural Networks (RNNs) use multiple video frames to train deep learning models to detect deepfakes. Statistical models can be used to compare the statistical differences between authentic and altered content and reduce the amount of training data.
Metadata recorded during content creation can be protected using blockchain technology to secure visual content. The tamperproof blockchain record ensures the authenticity of the content, providing a digital fingerprint of the image or video.
AI can be a robust and valuable tool, but it is still prone to errors, and as the amount of AI-generated content grows, validation becomes essential. The best way to minimize errors and bias is to validate the source data before using it for machine learning. Validate data sources for accuracy and be sure there is an extensive, balanced data sampling to minimize bias. Reliable deepfake detection solutions and tools like blockchain can be used to validate images and video and create a tamperproof fingerprint. AI is only as reliable as the source material used to train it. Take the extra steps to keep bias in check and eliminate deepfakes, which are rapidly becoming a new source of inaccuracies.
