How Training Data Influences AI Content Restrictions

Artificial intelligence (AI) is all around us today, in search engines, chatbots, tools that moderate user-created content, and even creative assistance software. Training data is one of the many elements that comprise the functioning of AI, and, in many respects, is one of the most misunderstood. The role of training data in shaping the restrictions placed on the content that AI systems are likely to generate, attempt to avoid, or how they choose to answer, especially on sensitive or controversial issues, is of utmost importance and needs to be critically analyzed.

 

What this piece intends to do is analyze the impact of training data on the content restrictions of AI, the reasons for such restrictions, and the implications of these restrictions for businesses, developers, and users in an AI-rich world.

Definition: What is the meaning of Training Data?

Training data can be defined as the basic material used to teach models how to identify patterns, generate language, and make decisions. Some examples of the types of materials that are used for the training data of models that process human language include the following:

  • Books, articles, and scholarly work
  • Web pages, online discussion forums, and documents available to the public
  • Conversational data and dialogue
  • Computer code and technical documentation

AI models are trained to identify and understand social norms, grammatical structures, relationships of fact, and context through the analysis of large volumes of texts.

AI may look like it is communicating with us as humans, however, this is much more an illusion than it is a reality. The way different AI systems work with human language is very different from the way we process human language. Instead, it simply understands the statistical associations that exist among the words and concepts.

Consequently, how AI systems operate, and the impositions that get placed, are determined by the characteristics, quality, and scope of the training data.

 

The Reason AI Systems Come Equipped With Content Restrictions

Before attempting to unravel the intricacies of how the scope and quality of training data impact the content restrictions of AI systems, we need to comprehend, on the most fundamental level, why restrictions are placed on AI content in the first place.

The core of this problem lies in the sheer fact that we need to address why the content restrictions are placed, before we even address the training data implications.

AI developers face restrictions because they need to:

  • Avoid system outputs that are threaded to produce systems that produce outputs that could be dangerous or illegal.
  • To mitigate systems that could propagate biased discrimination, hate speech, misinformation, or systems that inappropriately provide unsafe recommendations.
  • To comply with the legal requirements.
  • To protect the system’s user or the user of the system from the outputs that are uneasy or could be disturbing.

The fact that the training data is not enough to guarantee the AI will be safe, or operate so safely, is to be why some are not considered safe and are not allowed to be used to some systems that constrain how the AI uses learned behavior is restricted with the use of training data.

 

 

Relationship between content training data and content restrictions

 

1.   Exposure Defines Defining

AI systems are designed to be able to produce outputs based on the training data inputs that they received, and unless the training data is representative, or if the data is filtered or omitted from the inputs, the model will either:

  • Completely ignore the content, or
  • Provide such content in such a vague manner that it could trigger the system to refuse to provide the content.

Consider the example in which a training set of data lacks or has been excluded on content that is explicit in nature. The AI lacks the language skills to respond to the content in any detail, even if a user is prompted.

 

2.  Data Limitations Still Cause Stricter Controls

Content sourced from the internet is usually subjected to:

  • Cultural Bias
  • Stereotypes
  • Political Bias

Developers face these challenges head-on and usually implement even more strict content controls to stop AI from reproducing any further negative harm. This ensures:

  • Sensitive discussions activate safety controls
  • Answers are more vague and generalized
  • Some perspectives are closed off

So the higher the apparent bias and risk of the training data, the higher the content bias

 

Data Control: First in Line for Content Control

 

   I.         Pre-Training Data Filtering

Before the training cycle starts, data sets are skimmed and filtered to remove:

  • Speech that includes hate and extremist views
  • Very sexual content
  • Descriptions that include violence and graphic material
  • Anything that can identify your personal data, aka PII

This sets the tone for the behavior of AI. If certain content is excluded from training, the AI is incapable of replicating such content

 

II.         Data Filtering Challenges

While data filtering creates safety, it also creates:

  • Less understanding of the context of sensitive issues
  • Increased censorship of discussions that are historical or educational in nature
  • Less ability to objectively evaluate controversial topics

These systems operate under certain restrictions. Oftentimes, the restrictions that govern the operation of AI systems reflect the gaps in AI’s training, rather than content that is simply filtered during an interaction.

 

 

Reinforcement Learning with Human Feedback

 

➢  Learning from Non-Data Sources

The majority of contemporary AI is built on Reinforcement Learning from Human Feedback (RLHF). Human feedback is used to rate the response of AI in an attempt to steer the model in the desired direction.

This pathway affects the influence on the content moderation by:

  • analyzing acceptable responses
  • responding negatively to controversial or unsafe responses
  • promoting courteous, neutral, and non-aggressive communication

Regardless of the training data used and the presence of sensitive data, RLHF can ignore the data and train the model to be more constrained.

 

➢  Positive Human Values Maximize Restriction

Because RLHF is the use of human feedback, the model is trained with data that is imbued with:

  • Cultural Values
  • Primitive Ethics
  • Legal Norms

This is why some AI systems appear to be more restricted in a given language or location.

 

 

Data Training and Sensitivity of Topic

 

➢  High Risk Content Areas

Certain areas generally trigger content moderation due to data training limitations. These include:

  • health and mental health advice
  • legal content
  • political content
  • discussion of violence and self-harm
  • sexual content involving minors

The information in these areas is consistently and dangerously consistent with the information/used to train the systems.

Therefore, AI systems in these areas are trained to:

  • give disclaimers
  • Refer the user to a qualified person
  • not provide step-by-step guidance

The boundaries are primarily focused on management of risks as related to the quality of the data the model has been trained on, rather than censorship.

 

 

The Risks of misinformation and the reliability of data

 

➢  Outdated and Conflicting Data

Training datasets regularly contain contradicting and old data. If there were no boundaries, an AI could confidently present false content.

In such cases, developers impose:

  • The restriction of definitive answers to questions of not-fully-formed certainty
  • Factual information about real-time and beyond the cutoff time of the data
  • Instructions are dependent upon the accuracy of exactness

The absence of these boundaries is due to the data available for training not being reliable, albeit extensive.

 

 

Boundaries of a legal and ethical nature relative to data training

 

➢  Copyright and data ownership

Training datasets often contain elements of a copyrighted nature. To avoid legal liability, systems of AI may impose these restrictions:

  • Replication of copyrighted texts
  • Lyrics, and scripts of films and books
  • Methods and content that are proprietary and paywalled

These restrictions are a response to the manner in which copyrighted training sets are sourced and licensed.

 

➢  The preservation of privacy

When training datasets contain data of a personal nature, even unintentionally, systems of AI avoid:

  • The generation of information that is private
  • The guessing of personal information
  • The data of real persons that have been reconstructed.

This leads to prompts being answered in a very conservative manner. Particularly, when a request pertains to personal information.

 

 

How Training Data Impacts AI Refusals

 

Why AI Sometimes Says `I Can’t Help With That`

Refusals can come from:

  • Missing information
  • Data punishments
  • uncertainty and risk

It may be the case that in some instances it is best to not answer than to give information that may be accessed, which may result in a more harmful outcome. There are instances in which an AI is not able to answer a question because it operates under such strict principles.

 

 

The Balance: Confidence vs Anxiety

 

★   Continued Issues 

The balancing act is usually between:

  • unfettered growth and creativity
  • safety and public confidence

In most cases, unfettered growth is most useful, but also the most dangerous. Users often find that restrictions only add to their frustrations.

 

★   Responsive Content Restrictions

Unlike most AI systems that simply apply the same restrictions to all users, the most advanced systems adjust their restrictions based on:

  • The user’s Intent
  • The presentation of the issue
  • Educational vs. Malicious
  • Systems that rely on nuanced data epitomize modern AI.

 

 

Impacts on Users and Content Creators

 

➢  Users expectations

Positive Impacts Users: 

  • Better, more complex, restrictions.
  • Identifying AI assistants’ capabilities and restrictions.

 

Negative Impacts Users: 

  • Loss of the posited collaborations provided by the AI assistant.
  • Frustrations that arise from the use of restrictions.

 

➢  Considerations for Developers

Developers and companies working with AI have a lot at stake regarding the decisions they make when it comes to training data.

  • User trust
  • Regulatory compliance
  • Global usability
  • Brand reputation

The need to devise and practice responsible training data strategies now is a competitive and ethical need.

 

 

Conclusion

The importance of training data to the field of artificial intelligence cannot be overstated. AI training data is not just about figuring out what AI should be able to do, but also what it should not be allowed to do. When it comes to the use of AI, there are bound to be some restrictions, and these are not meant to be punitive, but rather to be responsible and balanced.

When it comes to filtering data, using the services of trained individuals, and the showcasing of artificial intelligence, there are bound to be ethical and legal considerations, all of which will impact the degree of restrictions imposed on the artificial intelligence being used by the customer.

The ever-present need to balance the level of limitations placed on a product with the extent of its usefulness will make the training of artificial intelligence one of the most significant factors to consider when determining what content should or should not be generated by artificial intelligence.

The complex restrictions AI have will result in a trade off. By harnessing the potential of AI, users will be able to work with it more effectively.