AI Training and Responsible AI

Why is Responsible AI Crucial for Business Content?

When a business’s LLM neglects responsible AI by creating intolerant, hateful, or biased content, it doesn’t just create the social issues mentioned above. The business may also suffer some consequences. Negative repercussions could occur with any public-facing content, including:

• Print marketing materials
• Official website chatbots
• Social media posts
• Sales emails
• Website copy

A company’s LLM is likelier to create offensive multilingual content if a human expert in the loop is absent from the process. In some cases, a human expert is essential to review and perfect AI translation or localization. These are the potential consequences a business may face:

Businesses may experience just one, or a combination, of these consequences. Taking the right steps to avoid these ramifications is crucial. Read our recommendations below. 

5 Tactics for Ensuring Responsible AI Usage and Preventing Harmful Content

Consider implementing all, or at least some, of these tactics to ensure your AI output isn’t unintentionally biased, racist, misogynistic, or simply offensive or culturally taboo. For optimal results, work with a diverse group of people throughout the AI training and monitoring process. They will bring a wider and stronger base of knowledge. Consider working with AI training experts, like the ones at Lionbridge, who combine expertise in AI, sociocultural norms, industries, and linguistics. Lastly, some companies may set policies for AI developers and users. These policies articulate consequences for misuse of an AI system. They motivate everyone to help ensure an AI never creates harmful or offensive content.

Tactic #1 Data Curation

When conducting AI training, proper data collection is crucial for teaching an LLM to create content free from bias, racism, misogyny, etc. Companies should take a two-pronged approach. Firstly, filter out data from sources that may include problematic viewpoints. The second step is ensuring the training data for an LLM represents a diverse array of voices and perspectives. If the content is multilingual or comes from differing locations or cultures, it may help to have local or linguistic experts assist with these tasks. Lionbridge has a solid foundation in linguistics and language. This expertise uniquely positions us to support the Natural Language Process required in machine learning. 

Tactic #2 Establish an Ethical Framework

When training AI for ethical output, building an ethical framework is essential. Much like creating a style guide or translation glossary, a company should develop a series of rules and guidelines that it would want all its content to abide by. Use industry standards to help develop the framework, ensuring compliance and better results. These frameworks may need to be expanded and varied for multilingual or cultural work to include new language and social norms or taboos. Companies should also set up protocols and structures for continuous ethical deployment of the AI model. 

Tactic #3 Ethical and Bias Pre-Training

During the pre-training and fine-tuning phases, companies should prioritize bias mitigation techniques. Using the ethical framework mentioned above, the LLM should be taught to identify and avoid creating and consuming biased or offensive content. When testing The LLM during pre-training, it’s essential to use data validation to update data sets with a foundational understanding of ethics and biases. The ethical framework is helpful for this step, as well.

During training, consider creating mechanisms that showcase the AI model’s decision-making when identifying and rejecting offensive content. This transparency will help later if there are issues.

Tactic #4: Continually Monitor Output

After training their AI, a company must still continue reviewing output. For mission-critical content, a human reviewer may be worth considering. This is particularly helpful for content designed for customers who speak different languages and come from other cultures. Companies may also want to use a human reviewer for regularly scheduled material audits to ensure quality and compliance with their ethical framework. You may also consider creating opportunities for customers to report offensive content, and incorporate this feedback into continuous fine-tuning efforts.

Tactic #5: Retrain as Needed

Companies should build retraining into their protocols for a couple of reasons. Firstly, the AI model may not thoroughly “learn” how to correctly apply the ethical framework initially. It may erroneously create offensive content, or the ethical framework itself might be lacking. A second reason for continued retraining is because cultural norms change constantly. While content isn’t offensive today, it could be tomorrow — especially if it's developed for customers who speak multiple languages or come from other cultures. The more cultures and languages, the more nuances required for an ethical framework.  

Get in touch

Start your AI training with Lionbridge’s experts. We’ve helped many clients get the most benefit out of their LLM. We take responsible AI usage and AI trust seriously, and we have our own TRUST framework. Trust us to ensure your LLM helps your company achieve its goals and brings in ROI. Let’s get in touch.