How do you ensure NLP models and applications are fair and diverse?

In my early days working with NLP, I was tasked with developing a chatbot for customer support. Excited, I jumped straight into coding without clear goals. The result? A bot that often misunderstood user queries, leading to customer frustration. I realized the importance of defining goals and values. On revisiting the project, I prioritized understanding our users' needs and ensuring the bot's responses aligned with our company's values. The revamped chatbot was not just technically sound but also resonated better with users, highlighting the significance of goal-setting in NLP projects.

When developing Natural Language Processing (NLP) models, addressing ethical concerns like bias and misrepresentation is crucial. Start by defining the goals and values aligning with ethical principles and societal responsibility. Collect diverse and representative data and ensure accurate labeling to combat bias. Regularly test and monitor model performance and impact to ensure fairness and diversity. Opt for models that are explainable and interpretable, ensuring transparency and accountability. Establish robust governance and uphold responsibility, keeping the project compliant with ethical standards and regulations, thereby promoting fairness and diversity in NLP applications.

My experience has been that AI systems reflect and often amplify the values of the people who built them. The adoption of powerful AI systems is a good time to reflect on the values of your organization. What is our treatment of our users and employees? Are we respectful of their privacy? Can we be more transparent? Do we value diversity? It may be possible to lay the foundation for systems that put people first by answering these questions.

In my experience after speaking with various parties small and big, several things get highlighted each time. - In order to ensure that the AI plays fair it’s an absolute must to have a data traceability of the model or algorithm. There is too much blackboxing going around in the market at the moment. - Data ownership and data privacy is another popular topic. With the rise of ChatGPT and other Sass AI services, companies are getting reticent towards incorporating AI into their current or future products just for the simple fact that 90% of the NLP ai solutions are using openai, and their customer data would just get sent over the wire towards a company that may or may not use the data for their own.

An educational tech firm I worked with exemplified this by developing an NLP tool aimed at enhancing online learning. Their goal was to personalize content for diverse student needs while upholding values of fairness and inclusivity. By identifying target users as remote learners and stakeholders as educators, they assessed potential impacts and established metrics for success, ensuring the application's alignment with ethical standards and fostering a commitment to transparent and equitable educational solutions.

A relatively small dataset is required for fine tuning large language models. As a result, you can focus on the quality of the data. In addition, you can fine tune with relative ease thanks to the many abstractions available.

For example, I helped a healthcare app developer scrutinized their data and methods when creating an NLP tool to interpret patient feedback. We meticulously examined data characteristics, ensuring diversity in patient demographics to avoid sampling bias. Annotations were made with clear guidelines to prevent inconsistencies, and multiple NLP methods were tested for optimal results. The focus was on managing ambiguities and handling noisy data, resulting in a reliable tool capable of extracting meaningful insights while minimizing biases, thus enhancing the app’s effectiveness in capturing diverse patient experiences.

Fairness becomes an issue when you have a large uncleaned dataset like the internet data. Because this is human-created data and humans are biased. This is not an issue when you train smaller NLP models like for sentiment analysis, as you can clean the data for any sort of biases. Data is the prime source of bias.

AI is driven by data, if data is bad then results from the model are unsatisfactory. This concept is directly applied to NLP, and LLM models as well. When you are training millions of text documents, it is hard to completely eradicate noise within textual data in order to achieve good results. Hence, incorporating the RLHF (Reinforcement Learning Human Feedback) model is necessary. By fine-tuning the model using human feedback, one can reduce hallucinations in the responses we get from NLP-based LLM models.

If you are using a pretrainned model ypu have to test it with different scenarios in order to identify their bias, this one works if you plan to use it in very specific scenarios. Otherwise, if you want to build you model upon data collection. You have to understand the sources of the data that you can get from, always bare in mind that sources have limitation and bias applied, so further down the road you can taking into account when doing analysis ot when auto generatign data from; because not always you can have access to a large set of data to work with

To embrace diversity and accessibility, I helped incorporate NLP to a translation app in order to serve users from various linguistic backgrounds. We decided to prioritize inclusivity, adding support for multiple languages and dialects, and considered cultural nuances to provide accurate translations. User interfaces were designed to be user-friendly, accommodating varying levels of digital literacy, and responsive to diverse user needs. Regular feedback loops were established, encouraging users from different demographics to share their preferences and experiences, which further refined the app, making it a versatile communication tool across different cultures and languages.

One trick I've found super handy is building in check steps at the prompt level. It's like a little safety net to ensure responses are more fair and inclusive.

NLP applications are unlike many other software where user inputs and behavior are free form and personalized, instead of clicking on particular buttons to complete a task Eg. Booking on Uber. While it is theoretically great to design for all people, realistically it is about ensuring that the target user groups are being mapped out well, and ensure that language, cultural, accessibility and technical ability levels are being considered. I found it useful to let different groups of external test users, especially those who do not work in the client company test the bot. Their feedback usually give us AHA moments that we would otherwise overlook.

A technical tip to increase diversity and accessibility is to use data augmentation techniques in order to generate more diverse data (i.e. for references to minorities that may be underrepresented in the original dataset). This can help mitigate bias by introducing variations in the training data and making the model more inclusive and fair.

I have tried to open the interaction with NLP, allowing people to naturallly interact with them. However, when building an specific task oriented NLP solution, you have to test and develop the best prompt for that specific task . Therefore I found valuable to allow user to express but in the backend categorize their selection between different options based on the results that they want. Contrary to what you naturally think this approach guarantee that the user gets want they looking for, limitating the frustating process in findinf the best prompt, by also allowing different people to get what they want. Because you have to remeber, NLP's works different according to the prompt that you enter.

To ensure that your NLP systems remain fair and diverse, it's essential to implement ongoing monitoring mechanisms that evaluate performance across various demographic groups. Static evaluations can miss nuances that evolve over time, given the dynamic nature of language and societal norms. Furthermore, engage with a diverse set of community stakeholders for periodic feedback. This could include experts on ethical AI, representatives from marginalized communities, and individuals with domain-specific knowledge relevant to your application. Their insights can help identify blind spots or biases in your NLP models that you might not be aware of, enabling you to make data-driven adjustments that uphold your defined goals and values.

Based on mye experience, in the dynamic world of NLP, regularly measuring the real-world impact is as vital as initial testing. For instance, a chatbot should continuously gauge its user interactions for effectiveness, user satisfaction, and potential areas of improvement. By actively seeking user feedback and analyzing performance metrics, developers can identify and rectify any shortcomings, ensuring the chatbot remains a reliable and responsible communication tool. This iterative process of refining based on real-world interactions enhances overall robustness and usability, aligning the application more closely with user needs and expectations.

While a model might perform well on a particular test set, that doesn't guarantee its fairness or generalizability across different demographics or use cases. It's essential to validate the model against multiple benchmarks that represent diverse user groups, languages, and scenarios. This helps in catching biases that might not be evident in a single benchmark, also it's not enough to just train the model; its performance and fairness should be validated on unseen data that represents the real-world scenarios where the model will be applied.

Keep a monitoring process at all times, looking at different scenarios of interactions. Identifying bias and if you can, retrain the model with data that answer the bias limitations of by adapting promps to model answers. You might think, both approach are kind of strong bias applied, and it is. Therefore, bias has to be driven by the goal that you want to achieve. * all of this apply for specific task oriented service, not as general purpose.

Testing and monitoring in NLP, especially with Generative AI (GenAI) which can produce unforeseen content, goes beyond mere accuracy checks. It's about understanding real-world implications and ensuring intended outputs match actual ones. Regular performance assessments against diverse datasets can uncover hidden biases or discrepancies. Furthermore, continuous monitoring helps gauge the societal impact and user perceptions. Feedback loops, both automated and human-reviewed, are essential. These insights not only refine the model but also uphold its ethical integrity. Ensuring that an NLP system, particularly GenAI, remains responsible and beneficial necessitates perpetual vigilance and adaptability.

I worked on a NLP project developing a healthcare chatbot designed to answer patient queries. As developers, we engaged healthcare professionals, patients, legal teams, and data security experts during development. We held workshops to gather diverse perspectives and suggestions, addressing concerns regarding data privacy, medical accuracy, and user accessibility. By doing so, we not only enhanced the chatbot’s functionality and trustworthiness but also fostered a sense of collective ownership and responsibility, ensuring the end result was well-rounded, fair, and met the diverse needs of the user base.

Having a multidisciplinary team work on contributing to the project is key to avoid tunnel vision or alienating certain customers’ needs. Engaging the front liners and giving weight to what they say is very valuable. However, we must also be mindful to not engage more stakeholders for the sake of it. I have seen cases where it adds unnecessary time, complicates the design process or resulted in having to water down the features/benefits to find a middle ground to incorporate sometimes conflicting comments.

In the ever-evolving landscape of Natural Language Processing (NLP) projects, success goes beyond lines of code and algorithms. It's about the collective responsibility of everyone involved or affected by these projects. Effective communication and collaboration are not just essential but transformative. There should have Clarity of Purpose: NLP projects should begin with a crystal-clear understanding of goals and values. To explain and justify these to diverse audiences, consider the 'why' behind your project. There should be collaboration for Data, Methods, and Performance: Transparency builds trust. and it break down complex technical details into digestible insights.

I realized the importance of effective communication and collaboration firsthand while we were navigating challenges in our project. It became evident that these practices not only offered invaluable insights and perspectives that might have otherwise eluded us but also distributed the workload efficiently and sparked the creation of innovative ideas and viewpoints for future projects.

In simple words: NLP projects should involve cross-functional teams, including -domain experts, -linguists, -ethicists, and -users, to ensure -diverse perspectives and -ethical considerations are integrated into the development process. This collaboration helps identify and address potential biases, ensuring NLP systems are fair and just.

NLP models will NEVER be completely fair. Bias is essential. For example, I ran a few experiments with ChatGPT, Bard, and Claude. They all have what I refer to as a 'pro-social' bias. They highly encourage socially beneficial actions, and in many cases, refuse to partake in writing about activities that could cause harm. This is a bias, but many would call it a good bias. Coders often can't eliminate bias - so they attempt to ensure any biases included in the product can be interpreted positively.

NLP are models to predict the next word in a sentence, same as you and me tried to predict the next word of the person that we have a conversation with. Thus this approach, same as we are is a result of our past experience (trainned data), there is always be a bias present in any NLP. Now think if you can have access to all the data in the world, you will get bias too. Because data is produce by human interaction and history written, large civilizations and power have drove the history and interactions data points, thus information. You either can use it or generate a new data based on some criteria, either way you are working with bias data

NLP, while a marvel of technology, is deeply intertwined with the human experience. Beyond algorithms, it's essential to remember the stories, emotions, and histories embedded in language. Each phrase and word carries a legacy, shaped by cultures, events, and individual tales. As we advance in NLP, let's remain curious, seeking not just patterns but the narratives they represent. Embracing this holistic view ensures that our NLP endeavors don't just process language, but honor its rich tapestry, celebrating the myriad voices that have shaped its evolution.

Ensuring fairness in NLP models requires diverse training data, bias detection, and mitigation techniques. Transparent model architectures help in understanding decision-making, while continuous evaluation ensures balanced performance across demographics. Incorporating human reviewers and diverse development teams can identify and address biases. Engaging with affected communities provides valuable feedback. Ethical guidelines, user customization, and education promote responsible development. Open collaboration with the research community and continuous real-world monitoring are essential. As societal norms evolve, continuous updates are crucial to maintain fairness in NLP systems.

Considering diversity and fairness in NLP applications are very important to provide fair and inclusive services. Consider this simple example: ??Think of a smart assistant on phones. ??If it can only understand and respond to people who speak one language, it excludes everyone else. ??To make it fair and useful for all users, it should be able to understand and assist people who speak different languages. ??This way, it's inclusive and helpful to a wider range of people.