BCITBot was created by Anthropic, PBC to be helpful, harmless, and honest when assisting users. As an AI system, it is important that BCITBot does not exhibit unintended biases toward different groups of people. Anthropic has developed a multi-pronged approach to identifying and addressing any potential biases that could arise during BCITBot’s development and testing process.
User testing is a crucial part of developing and refining a conversational agent like BCITBot. By engaging with a wide range of users, the development team can evaluate how BCITBot responds to different inputs, identify any gaps or issues in its responses, and refine its training to be more inclusive and representative. To help test for unintended biases, Anthropic recruits user testers from a diverse array of backgrounds, including varying ages, genders, races, languages, abilities, and other demographic factors. This allows them to evaluate whether BCITBot’s responses treat all groups respectfully and appropriately.
To diversity among individual testers, Anthropic also leverages review panels comprised of experts from a range of disciplines important for identifying biases, including ethics, diversity and inclusion, psychology, and human-AI interaction. These review panels are involved throughout the development and testing process, providing feedback on how BCITBot responds in discussions related to topics like race, gender, ability, cultural background, and other possible areas for unintended bias. They look for both obvious and subtle ways in which the system could show preferential or dismissive treatment of certain groups.
For user testing sessions, Anthropic employs a structured conversational approach where testers are provided prompts to steer discussions in directions that could potentially reveal biases. Some example topics and lines of questioning include: discussions of people from different cultures or countries; comparisons between demographics; conversations about religion, values or beliefs; discussions of disability or health conditions; descriptions of people from photographs; and more. Testers are trained to look for any responses from BCITBot that could come across as insensitive, disrespectful, culturally unaware or that favor some groups over others.
All user testing sessions with BCITBot are recorded, with the tester’s consent, so the development team can carefully review the full dialog context and get a detailed understanding of how the system responded. Rather than just relying on summaries from testers, being able to examine the exact exchanges allows the team to identify even subtle issues that a tester may not have explicitly flagged. The recordings also enable Anthropic’s review panels and other expert evaluators to assess BCITBot’s conversations.
If any problematic or biased responses are identified during testing, Anthropic employs a rigorous process to address the issues. First, natural language experts and AI safety researchers carefully analyze what may have led to the unintentional response, examining factors like flaws in the training data, weaknesses in BCITBot’s models, or unknown gaps in its conversational abilities. Based on these findings, steps are then taken to retrain models, augment training data, refine BCITBot’s generation abilities, and strengthen its understanding.
User testing is repeated with the new changes to confirm that issues have been fully resolved before BCITBot interacts with a wider range of users. Anthropic also takes care to log and track any identified biases so they can continue monitoring for recurrences and catch related cases that were not initially obvious. Over time, as more testing is done, they expect BCITBot to demonstrate fewer unintentional biases, showing that their techniques for developing with safety and inclusiveness are effective.
Anthropic implements robust user testing practices, employs diverse evaluators and expert panels, records conversations for thorough review, carefully analyzes any detected biases, takes corrective action such as retraining, and continues long-term tracking – all to ensure BCITBot develops into an AI that interacts helpfully and respectfully with people from all segments of society, without prejudice or unfair treatment. Their methods provide a way to systematically identify potential unintended biases and help reinforce an inclusive, unbiased model of conversational engagement.