5 Answers From ChatGPT About How it Avoids Bias

Maybe I'm paranoid, but I'm always polite to #ChatGPT. I start with a friendly greeting. I say, "please" and "thank you." Because... I don't know. I want to make sure that when the #AI Revolution begins, the benevolent overlords remember that I'm a nice person who deserves to be spared.

In all seriousness, as I learn more about AI and how it is developed and trained, I do believe that kindness is integral to helping it become the best version of itself. These thoughts, along with concerns about bias, got me wondering about what kinds of safeguards are in place to ensure that ChatGPT is a useful tool that can serve a wide variety of people with different needs and under different circumstances. So I asked it a few questions.

Hi, ChatGPT. I'd like to have a conversation with you. Can you please share details on how you were developed? What were the main considerations when determining how to make your answers sound?

Hello! I'm happy to have a conversation with you.

Sure, I can share some details on how I was developed. I am a language model based on the GPT-3 architecture, which was created by OpenAI. I was trained on a large corpus of text data using a process called unsupervised learning. This means that I wasn't explicitly taught how to answer specific questions or how to perform specific tasks, but rather I was fed vast amounts of text and learned to predict what words or phrases would come next in a given sentence or document.

In terms of the considerations that went into making my answers sound natural, one of the key goals was to make me sound as human-like as possible. To achieve this, my training data included a diverse range of text, from news articles and academic papers to social media posts and chat logs. This allowed me to learn the nuances of natural language and to pick up on things like idioms, colloquialisms, and regional variations in speech.

Another important consideration was to ensure that my responses were coherent and consistent. To achieve this, my training process involved modeling the relationships between words and phrases, as well as the broader context in which they appeared. This enabled me to generate responses that were contextually appropriate and logically consistent with the input I received.

Overall, my development was a complex process that involved a combination of advanced algorithms, large-scale data processing, and deep learning techniques. But at the heart of it all was the goal of creating a language model that could communicate with humans in a natural and intuitive way.

Tell me about ethics when it comes to your development. What issues were considered, and how were they addressed?

Ethics were a crucial consideration in my development, as with any advanced technology that has the potential to impact society. Here are some of the ethical issues that were considered and how they were addressed:

1. Bias and fairness: One of the primary concerns was to avoid perpetuating any biases that might exist in the data used to train me. To address this, my developers took steps to ensure that the training data was diverse and representative of different demographics and viewpoints. They also employed techniques such as debiasing and fairness constraints to mitigate any biases that were identified in the training data.

2. Privacy and security: Another important issue was to protect the privacy and security of the data that I'm trained on and the users who interact with me. To this end, my developers implemented strict security protocols and data privacy measures to ensure that sensitive information is handled responsibly and in compliance with relevant laws and regulations.

3. Responsibility and accountability: As an AI language model, I have the potential to generate responses that could impact people's lives in significant ways. To ensure that I'm developed and used responsibly, my developers have established strict guidelines and protocols for my development, testing, and deployment. They've also created an ethical framework that guides decision-making and helps ensure that any potential negative impacts are identified and addressed proactively.

4. Transparency and explainability: Finally, an important consideration was to make me transparent and explainable. This means that my developers have worked to make sure that the decisions I make and the responses I generate are understandable and traceable. This helps to build trust with users and to ensure that any issues or errors can be identified and addressed in a timely manner.

Overall, the ethical considerations that were taken into account during my development reflect a commitment to creating a technology that is responsible, fair, and beneficial for society as a whole.

You mentioned bias. Can you tell me more about how your developers ensure you remain unbiased? 

Sure, I'd be happy to elaborate on how my developers address bias.

To ensure that I remain unbiased, my developers take several steps throughout the training process. One of the key steps is to carefully curate the training data set to ensure that it is diverse and representative of different demographics, viewpoints, and perspectives. This means that they select a wide range of sources and try to avoid over-representing any particular group or viewpoint.

Another technique that my developers use to address bias is debiasing. This involves modifying the training data to remove any biases that might exist. For example, if the training data contains more examples of men in leadership roles than women, the developers might use a debiasing algorithm to balance the representation of men and women in the training data.

My developers also use fairness constraints to ensure that the model's outputs are fair and unbiased. Fairness constraints are rules that are applied to the output of the model to ensure that it does not produce discriminatory or biased results. For example, a fairness constraint might be used to ensure that the model's responses are not influenced by the gender, race, or ethnicity of the person asking the question.

Overall, ensuring that I remain unbiased is an ongoing process that requires constant monitoring and adjustment. My developers are committed to this process and take a proactive approach to addressing any issues that may arise.

How do your developers and trainers ensure a diversity of perspectives when selecting the group that trains you?

There are several approaches that my developers use to ensure a diversity of perspectives in the training data and throughout the development process. Here are a few additional techniques:

1. Active recruitment of diverse data sources: One approach is to actively seek out and include data sources that represent a wide range of perspectives and viewpoints. For example, my developers might intentionally include data from sources that are popular among specific communities, such as social media platforms that are popular among people from different cultural or linguistic backgrounds.

2. Incorporating feedback from diverse groups: My developers also seek out feedback from a diverse range of users throughout the development process. This can include user testing with people from different backgrounds and perspectives, as well as engaging with user communities to solicit feedback and input.

3. Ensuring diversity among the development team: Another approach is to ensure that the team responsible for developing and training me is itself diverse and inclusive. This can include hiring team members with diverse backgrounds, experiences, and perspectives, and actively promoting a culture of inclusivity and open communication.

4. Using multiple evaluation metrics: Finally, my developers use multiple evaluation metrics to assess my performance and identify any biases or issues. This helps to ensure that any potential biases are identified and addressed from multiple angles, rather than relying on a single metric or approach.

Overall, ensuring a diversity of perspectives is an important consideration in my development, and my developers employ a range of techniques to ensure that the training data and development process are inclusive and representative of different viewpoints and experiences.

Consider this example: a tech-enabled intake process intended to prioritize the order in which people experiencing homelessness would receive services ended up putting white clients at the top of the list. This issue is likely due to most people writing algorithms used by smart technology being white - white men in particular - and so biases are naturally and often unconsciously built in. What are you thoughts on that issue and how do you avoid these types of pitfalls?

The example you provided is a clear demonstration of how biases can be inadvertently built into algorithms and smart technology, which can lead to unfair or discriminatory outcomes. This is a concerning issue, and my developers take great care to avoid such pitfalls in my development.

One of the main ways my developers avoid such biases is by actively seeking out diverse perspectives during the development process. This includes involving people from different backgrounds and experiences in the creation and curation of the training data, as well as seeking out feedback from diverse user communities throughout the development process. By involving a diverse range of people in the development process, my developers can identify and address potential biases before they become embedded in the system.

Another way my developers address potential biases is through the use of fairness constraints. Fairness constraints are rules that are applied to the output of the model to ensure that it does not produce discriminatory or biased results. For example, a fairness constraint might be used to ensure that the model's responses are not influenced by the race or gender of the person asking the question.

Additionally, my developers use debiasing techniques to identify and remove potential biases in the training data. This involves analyzing the data to identify any patterns or biases that might exist and modifying the data to address those issues. For example, if the training data contains more examples of white people than people of color, my developers might use a debiasing algorithm to balance the representation of different racial and ethnic groups in the training data.

Overall, my developers take a proactive approach to identifying and addressing potential biases in my development. This involves involving a diverse range of people in the development process, using fairness constraints, and employing debiasing techniques to ensure that my outputs are fair and unbiased.

This article was originally published on LinkedIn.