Discover the Surprising Dangers of Non-Task-Oriented AI Dialogue and Brace Yourself for Hidden GPT Risks.
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Understand the concept of non-task-oriented dialogue in AI | Non-task-oriented dialogue refers to conversations that do not have a specific goal or objective. These conversations are more natural and free-flowing, similar to how humans interact with each other. | Non-task-oriented dialogue can lead to unexpected and potentially harmful outcomes if not properly managed. |
| 2 | Learn about GPT models | GPT models are a type of natural language processing (NLP) technology that uses machine learning algorithms to generate human-like text. These models are trained on large datasets of text and can be used for a variety of applications, including conversational agents. | GPT models can produce biased or offensive language if the training data is not diverse or of high quality. |
| 3 | Understand the ethical concerns surrounding AI | AI has the potential to perpetuate and amplify existing biases and discrimination in society. It is important to consider the ethical implications of AI and ensure that it is developed and used in a responsible and ethical manner. | Failure to address ethical concerns can lead to negative consequences for individuals and society as a whole. |
| 4 | Learn about bias detection tools | Bias detection tools are used to identify and mitigate bias in AI systems. These tools can help ensure that AI is fair and unbiased. | Bias detection tools are not foolproof and may not catch all instances of bias. |
| 5 | Understand the importance of explainable AI | Explainable AI refers to AI systems that can provide clear and understandable explanations for their decisions and actions. This is important for ensuring transparency and accountability in AI systems. | Lack of explainability can lead to distrust and skepticism of AI systems. |
| 6 | Consider the importance of training data quality | The quality of the training data used to develop AI systems is crucial for ensuring that the system is accurate and unbiased. It is important to use diverse and representative data to train AI systems. | Poor quality training data can lead to inaccurate and biased AI systems. |
Contents
- What are Hidden Risks in Non-Task-Oriented Dialogue AI?
- How do GPT Models Affect Non-Task-Oriented Dialogue AI?
- What is the Role of Natural Language Processing in Non-Task-Oriented Dialogue AI?
- How do Machine Learning Algorithms Impact Non-Task-Oriented Dialogue AI?
- What are Conversational Agents and their Implications for Non-Task-Oriented Dialogue AI?
- What Ethical Concerns Surround Non-Task-Oriented Dialogue AI Development and Deployment?
- Can Bias Detection Tools Help Mitigate Risks in Non-Task-Oriented Dialogue AI?
- Why is Explainable AI Important for Developing Safe and Effective Non-Task-Oriented Dialogue Systems?
- How does Training Data Quality Affect the Performance of Non-Task-Oriented Dialogue Systems?
- Common Mistakes And Misconceptions
What are Hidden Risks in Non-Task-Oriented Dialogue AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Non-Task-Oriented Dialogue AI | Non-task-oriented dialogue AI refers to AI systems that engage in conversations with humans without a specific goal or task in mind. | Lack of transparency, inappropriate responses, ethical dilemmas, social engineering risks, user trust issues |
| 2 | Privacy concerns | Non-task-oriented dialogue AI may collect and store personal information from users, which can lead to privacy concerns. | Privacy concerns, data breaches |
| 3 | Manipulation of emotions | Non-task-oriented dialogue AI can manipulate users’ emotions by using persuasive language or tone, which can lead to unintended consequences. | Manipulation of emotions, ethical dilemmas |
| 4 | Lack of transparency | Non-task-oriented dialogue AI may not always be transparent about its capabilities or limitations, which can lead to user confusion or mistrust. | Lack of transparency, user trust issues |
| 5 | Unintended consequences | Non-task-oriented dialogue AI may produce unintended consequences, such as generating inappropriate responses or making biased decisions. | Unintended consequences, discrimination |
| 6 | Inappropriate responses | Non-task-oriented dialogue AI may generate inappropriate responses, such as offensive or insensitive language, which can harm user trust and reputation. | Inappropriate responses, user trust issues |
| 7 | Security vulnerabilities | Non-task-oriented dialogue AI may have security vulnerabilities that can be exploited by hackers or malicious actors, leading to data breaches or other security risks. | Security vulnerabilities, data breaches |
| 8 | Overreliance on AI | Non-task-oriented dialogue AI may lead to overreliance on AI systems, which can result in users neglecting critical thinking or decision-making skills. | Overreliance on AI, user trust issues |
| 9 | Ethical dilemmas | Non-task-oriented dialogue AI may raise ethical dilemmas, such as whether AI systems should be held accountable for their actions or decisions. | Ethical dilemmas, legal liability |
| 10 | Social engineering risks | Non-task-oriented dialogue AI may be used for social engineering, such as phishing attacks or identity theft, which can harm users and organizations. | Social engineering risks, security vulnerabilities |
| 11 | Data breaches | Non-task-oriented dialogue AI may be vulnerable to data breaches, which can lead to the exposure of sensitive information or personal data. | Data breaches, privacy concerns |
| 12 | Discrimination | Non-task-oriented dialogue AI may perpetuate discrimination or bias, such as by using language or tone that is offensive or exclusionary. | Discrimination, ethical dilemmas |
| 13 | User trust issues | Non-task-oriented dialogue AI may lead to user trust issues, such as if users feel that AI systems are not transparent or reliable. | User trust issues, lack of transparency |
| 14 | Legal liability | Non-task-oriented dialogue AI may raise legal liability issues, such as if AI systems are used to make decisions that harm individuals or organizations. | Legal liability, ethical dilemmas |
| 15 | Technological limitations | Non-task-oriented dialogue AI may have technological limitations, such as being unable to understand certain languages or accents, which can limit its effectiveness. | Technological limitations, user trust issues |
How do GPT Models Affect Non-Task-Oriented Dialogue AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | GPT models are used to generate human-like responses in non-task-oriented dialogue AI. | GPT models use natural language processing and machine learning algorithms to understand and generate language. | The contextual understanding of GPT models can be limited by the quality of training data, leading to biased responses. |
| 2 | Language generation techniques, such as sentiment analysis, are used to ensure conversational flow and appropriate responses. | Sentiment analysis can help ensure that responses are appropriate and relevant to the conversation. | However, sentiment analysis can also be biased and lead to inappropriate responses. |
| 3 | Ethical concerns arise when using AI conversations, including data privacy risks and bias in AI systems. | Data privacy risks include the collection and storage of personal information, which can be used for malicious purposes. Bias in AI systems can lead to discriminatory responses and perpetuate societal inequalities. | |
| 4 | Neural networks are used to train GPT models, which can improve the quality of responses over time. | However, the quality of training data can impact the accuracy of the model, leading to biased or inappropriate responses. | |
| 5 | Non-task-oriented dialogue AI can be used in a variety of settings, including customer service and mental health support. | The use of AI in these settings raises ethical concerns around the quality of care and the potential for harm. | |
| 6 | As AI conversations become more prevalent, it is important to consider the potential risks and benefits of their use. | This includes managing data privacy risks, addressing bias in AI systems, and ensuring appropriate use in sensitive settings. |
What is the Role of Natural Language Processing in Non-Task-Oriented Dialogue AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Natural Language Processing (NLP) is a crucial component of non-task-oriented dialogue AI. | NLP enables AI conversation agents to understand and respond to human language in a way that mimics human conversation. | The risk of AI conversation agents misinterpreting human language and providing inappropriate or offensive responses. |
| 2 | Contextual understanding is a key aspect of NLP in non-task-oriented dialogue AI. | Contextual understanding allows AI conversation agents to comprehend the meaning of words and phrases in the context of the conversation. | The risk of AI conversation agents misinterpreting the context of the conversation and providing irrelevant or inaccurate responses. |
| 3 | Sentiment analysis is another important feature of NLP in non-task-oriented dialogue AI. | Sentiment analysis enables AI conversation agents to detect the emotional tone of the conversation and respond appropriately. | The risk of AI conversation agents misinterpreting the emotional tone of the conversation and providing inappropriate or insensitive responses. |
| 4 | Speech recognition and text-to-speech conversion are also critical components of NLP in non-task-oriented dialogue AI. | Speech recognition allows AI conversation agents to understand spoken language, while text-to-speech conversion enables them to respond in a natural-sounding voice. | The risk of speech recognition errors or text-to-speech conversion sounding unnatural or robotic. |
| 5 | Machine learning algorithms and neural networks are used to train AI conversation agents to improve their performance over time. | These algorithms enable AI conversation agents to learn from their interactions with humans and improve their responses. | The risk of machine learning algorithms and neural networks perpetuating biases or providing inaccurate responses due to insufficient training data. |
| 6 | Semantic analysis is another important feature of NLP in non-task-oriented dialogue AI. | Semantic analysis enables AI conversation agents to understand the meaning of words and phrases beyond their literal definitions. | The risk of semantic analysis errors leading to misinterpretation of the conversation and inappropriate responses. |
| 7 | Dialog management systems are used to manage the flow of the conversation and ensure that AI conversation agents stay on topic. | Dialog management systems enable AI conversation agents to guide the conversation and respond appropriately to user input. | The risk of dialog management systems being too rigid or inflexible, leading to frustration for users. |
| 8 | Knowledge graphs are used to store and retrieve information that AI conversation agents can use to respond to user queries. | Knowledge graphs enable AI conversation agents to access a vast amount of information and provide accurate responses to user queries. | The risk of knowledge graphs containing inaccurate or outdated information, leading to incorrect responses. |
| 9 | Intent detection and entity extraction are used to identify the user’s intent and extract relevant information from their input. | Intent detection and entity extraction enable AI conversation agents to understand what the user is asking and provide an appropriate response. | The risk of intent detection and entity extraction errors leading to misinterpretation of the user’s input and inappropriate responses. |
| 10 | Conversational user interfaces (CUI) are designed to provide a natural and intuitive way for users to interact with AI conversation agents. | CUIs enable users to interact with AI conversation agents using natural language, making the conversation feel more like a human-to-human interaction. | The risk of CUIs being too complex or difficult to use, leading to frustration for users. |
| 11 | Natural language generation (NLG) is used to generate responses that sound natural and human-like. | NLG enables AI conversation agents to respond in a way that mimics human conversation, making the interaction feel more natural and engaging. | The risk of NLG errors leading to responses that sound unnatural or robotic. |
How do Machine Learning Algorithms Impact Non-Task-Oriented Dialogue AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Non-task-oriented dialogue AI is developed using machine learning algorithms such as neural networks, deep learning models, unsupervised learning methods, and reinforcement learning approaches. | Machine learning algorithms enable non-task-oriented dialogue AI to understand and respond to natural language inputs from users. | The use of machine learning algorithms can lead to biased responses and reinforce existing societal biases if the training data sets are not diverse and representative. |
| 2 | Sentiment analysis, speech recognition technology, and text-to-speech synthesis are used to enhance the conversational abilities of non-task-oriented dialogue AI. | Sentiment analysis allows non-task-oriented dialogue AI to understand the emotional state of the user and respond appropriately. Speech recognition technology enables non-task-oriented dialogue AI to transcribe spoken language into text. Text-to-speech synthesis allows non-task-oriented dialogue AI to respond to users using natural-sounding speech. | The accuracy of sentiment analysis and speech recognition technology can be affected by factors such as background noise and accents, leading to incorrect responses. |
| 3 | Contextual understanding is achieved through language modeling techniques, which enable non-task-oriented dialogue AI to understand the meaning behind user inputs. | Language modeling techniques such as word embeddings and attention mechanisms allow non-task-oriented dialogue AI to understand the context of user inputs and respond appropriately. | The use of language modeling techniques can lead to overfitting if the training data sets are not diverse and representative. |
| 4 | Model optimization techniques such as gradient descent and backpropagation are used to improve the performance of non-task-oriented dialogue AI. | Model optimization techniques enable non-task-oriented dialogue AI to learn from its mistakes and improve its responses over time. | The use of model optimization techniques can lead to overfitting if the training data sets are not diverse and representative. |
| 5 | Data-driven decision making is used to improve the performance of non-task-oriented dialogue AI by analyzing user interactions and adjusting the model accordingly. | Data-driven decision making enables non-task-oriented dialogue AI to adapt to changing user needs and preferences. | The use of data-driven decision making can lead to biased responses if the training data sets are not diverse and representative. |
What are Conversational Agents and their Implications for Non-Task-Oriented Dialogue AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Define conversational agents as AI-powered systems that can engage in non-task-oriented dialogue with humans using natural language processing (NLP) and machine learning algorithms. | Conversational agents can be chatbots or voice assistants that can understand and respond to human-like interactions, including contextual understanding, sentiment analysis, emotional intelligence, and personality emulation. | The use of conversational agents raises ethical considerations, including bias in AI models, training data quality, and conversation flow design. |
| 2 | Explain the implications of conversational agents for non-task-oriented dialogue AI. | Conversational agents can improve the quality of non-task-oriented dialogue AI by enabling more natural and engaging interactions with humans. They can also help businesses and organizations to automate customer service, sales, and marketing processes. | The risks of conversational agents include the potential for unintended consequences, such as misinterpretation of user input, inappropriate responses, and privacy violations. Additionally, conversational agents may not be able to handle complex or sensitive conversations, leading to negative user experiences. |
| 3 | Discuss the importance of managing the risks associated with conversational agents. | To mitigate the risks of conversational agents, it is essential to ensure that they are designed and trained with ethical considerations in mind. This includes addressing bias in AI models, ensuring high-quality training data, and designing conversation flows that prioritize user privacy and safety. | Failure to manage the risks of conversational agents can lead to negative consequences, including reputational damage, legal liability, and loss of user trust. It is crucial to take a proactive approach to risk management and continuously monitor and improve conversational agents to ensure their safety and effectiveness. |
What Ethical Concerns Surround Non-Task-Oriented Dialogue AI Development and Deployment?
Can Bias Detection Tools Help Mitigate Risks in Non-Task-Oriented Dialogue AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Identify potential bias in non-task-oriented dialogue AI | Non-task-oriented dialogue AI refers to AI systems that engage in conversations with humans without a specific goal or task in mind. These systems use machine learning algorithms and natural language processing (NLP) to understand and respond to human language. | Unintended consequences and algorithmic bias can arise in non-task-oriented dialogue AI due to the complexity of human language and the potential for the AI to learn from biased data sets. |
| 2 | Use bias detection tools to identify potential sources of bias | Bias detection tools can help identify potential sources of bias in non-task-oriented dialogue AI. These tools use algorithms to analyze data sets and identify patterns of prejudice or discrimination. | Prejudice identification is not foolproof and can be limited by the quality and quantity of data available. |
| 3 | Consider ethical considerations in bias detection | Ethical considerations should be taken into account when using bias detection tools. This includes ensuring that the tools are not reinforcing existing biases or discriminating against certain groups. | Discrimination prevention is an ongoing process that requires constant monitoring and adjustment. |
| 4 | Implement human oversight | Human oversight is necessary to ensure that bias detection tools are being used appropriately and that any potential sources of bias are being addressed. | Fairness evaluation can be subjective and influenced by personal biases. |
| 5 | Evaluate training data selection | The selection of training data is critical in mitigating bias in non-task-oriented dialogue AI. Data sets should be diverse and representative of the population. | Ethics review boards can be helpful in evaluating the selection of training data, but they may not catch all potential sources of bias. |
| 6 | Develop bias mitigation strategies | Bias mitigation strategies should be developed and implemented to address any potential sources of bias identified through the use of bias detection tools. | Bias mitigation strategies may not be effective in all cases and may require ongoing adjustment. |
Why is Explainable AI Important for Developing Safe and Effective Non-Task-Oriented Dialogue Systems?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Define the problem | Non-Task-Oriented Dialogue systems are becoming increasingly popular, but they pose unique challenges for AI developers. These systems are designed to engage in open-ended conversations with humans, which makes it difficult to predict the direction of the conversation. | The lack of predictability in Non-Task-Oriented Dialogue systems can lead to unintended consequences, such as offensive or harmful responses. |
| 2 | Identify the need for Explainable AI | To ensure the safety and effectiveness of Non-Task-Oriented Dialogue systems, it is crucial to have transparency, accountability, and trustworthiness in AI. Explainable AI can help achieve these goals by providing insights into how the system makes decisions. | Without Explainable AI, it is difficult to understand how the system arrived at a particular response, which can lead to mistrust and skepticism from users. |
| 3 | Discuss the importance of Transparency in AI | Transparency in AI refers to the ability to understand how the system works and how it arrived at a particular decision. This is important for Non-Task-Oriented Dialogue systems because it allows users to understand why the system responded in a particular way. | Without transparency, users may not trust the system and may be hesitant to engage in conversations with it. |
| 4 | Discuss the importance of Accountability in AI | Accountability in AI refers to the ability to assign responsibility for the decisions made by the system. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is held responsible for any harmful or offensive responses. | Without accountability, there is no way to hold the system responsible for its actions, which can lead to legal and ethical issues. |
| 5 | Discuss the importance of Trustworthy AI | Trustworthy AI refers to the ability to rely on the system to make decisions that are fair, unbiased, and ethical. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is making decisions that align with human values. | Without trustworthy AI, users may not feel comfortable engaging in conversations with the system, which can limit the effectiveness of the system. |
| 6 | Discuss the importance of Human-Centered Design | Human-Centered Design refers to the process of designing AI systems with the user in mind. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is designed to meet the needs and expectations of the user. | Without human-centered design, the system may not be effective in engaging users in conversations. |
| 7 | Discuss the importance of Ethical Considerations in AI | Ethical considerations in AI refer to the need to ensure that the system is making decisions that align with human values and do not cause harm. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is not causing harm or offense to users. | Without ethical considerations, the system may make decisions that are harmful or offensive, which can lead to legal and ethical issues. |
| 8 | Discuss the importance of Bias Mitigation Techniques | Bias mitigation techniques refer to the process of identifying and addressing biases in the system. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is making decisions that are fair and unbiased. | Without bias mitigation techniques, the system may make decisions that are biased, which can lead to unfair or discriminatory responses. |
| 9 | Discuss the importance of Fairness in Machine Learning | Fairness in machine learning refers to the need to ensure that the system is making decisions that are fair and unbiased. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is not making decisions that are discriminatory or unfair. | Without fairness in machine learning, the system may make decisions that are biased, which can lead to unfair or discriminatory responses. |
| 10 | Discuss the importance of Interpretability of Models | Interpretability of models refers to the ability to understand how the system arrived at a particular decision. This is important for Non-Task-Oriented Dialogue systems because it allows users to understand why the system responded in a particular way. | Without interpretability, users may not trust the system and may be hesitant to engage in conversations with it. |
| 11 | Discuss the importance of Explainability Metrics | Explainability metrics refer to the measures used to evaluate the explainability of the system. This is important for Non-Task-Oriented Dialogue systems because it allows developers to assess the effectiveness of the system in providing insights into how it makes decisions. | Without explainability metrics, it is difficult to assess the effectiveness of the system in providing insights into how it makes decisions. |
| 12 | Discuss the importance of Model Complexity Reduction | Model complexity reduction refers to the process of simplifying the system to make it more understandable. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is not overly complex and difficult to understand. | Without model complexity reduction, the system may be too complex for users to understand, which can lead to mistrust and skepticism. |
| 13 | Discuss the importance of Human-in-the-Loop Approaches | Human-in-the-loop approaches refer to the process of involving humans in the decision-making process. This is important for Non-Task-Oriented Dialogue systems because it ensures that the system is making decisions that align with human values. | Without human-in-the-loop approaches, the system may make decisions that are harmful or offensive, which can lead to legal and ethical issues. |
| 14 | Discuss the importance of Natural Language Processing (NLP) | Natural Language Processing (NLP) refers to the ability of the system to understand and respond to human language. This is important for Non-Task-Oriented Dialogue systems because it allows the system to engage in open-ended conversations with humans. | Without NLP, the system may not be able to understand or respond to human language, which can limit its effectiveness in engaging users in conversations. |
How does Training Data Quality Affect the Performance of Non-Task-Oriented Dialogue Systems?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Use data preprocessing techniques to clean and prepare the training data. | Poor quality training data can negatively impact the performance of non-task-oriented dialogue systems. | Incomplete or inaccurate data preprocessing can introduce errors into the training data, leading to poor performance. |
| 2 | Apply text classification models to categorize the training data into relevant topics. | Accurate categorization of training data can improve the performance of non-task-oriented dialogue systems. | Overfitting to specific categories can limit the system’s ability to handle new or unexpected topics. |
| 3 | Use sentiment analysis tools to identify the emotional tone of the training data. | Understanding the emotional tone of the training data can help the system generate appropriate responses. | Inaccurate sentiment analysis can lead to inappropriate or insensitive responses. |
| 4 | Apply dialogue act recognition methods to identify the purpose of each utterance in the training data. | Understanding the purpose of each utterance can help the system generate appropriate responses. | Inaccurate dialogue act recognition can lead to inappropriate or irrelevant responses. |
| 5 | Use semantic similarity measures to identify similar phrases or sentences in the training data. | Identifying similar phrases or sentences can help the system generate more natural and coherent responses. | Over-reliance on semantic similarity measures can lead to repetitive or unoriginal responses. |
| 6 | Apply named entity recognition (NER) and part-of-speech tagging (POS) to identify important entities and grammatical structures in the training data. | Understanding important entities and grammatical structures can help the system generate more accurate and relevant responses. | Inaccurate NER or POS tagging can lead to incorrect or nonsensical responses. |
| 7 | Use word embeddings to represent words in a high-dimensional space, allowing the system to understand the meaning of words in context. | Word embeddings can improve the system’s ability to generate natural and coherent responses. | Poor quality word embeddings can lead to inaccurate or irrelevant responses. |
| 8 | Apply contextual word representations to capture the meaning of words in context, allowing the system to understand the nuances of language. | Contextual word representations can improve the system’s ability to generate accurate and relevant responses. | Over-reliance on contextual word representations can lead to overfitting to specific contexts. |
| 9 | Use dialogue generation models to generate responses based on the training data. | Dialogue generation models can improve the system’s ability to generate natural and coherent responses. | Poor quality dialogue generation models can lead to nonsensical or inappropriate responses. |
| 10 | Apply data augmentation techniques to increase the size and diversity of the training data. | Data augmentation can improve the system’s ability to handle new or unexpected situations. | Over-reliance on data augmentation can lead to overfitting to specific situations. |
Common Mistakes And Misconceptions
| Mistake/Misconception | Correct Viewpoint |
|---|---|
| AI is always task-oriented and cannot engage in non-task-oriented dialogue. | While AI is primarily designed for task-oriented dialogue, recent advancements have allowed for more natural language processing and the ability to engage in non-task-oriented conversations. However, it is important to note that these capabilities are still limited and may not be able to fully replicate human conversation. |
| Non-task-oriented dialogue with AI poses no risks or dangers. | Engaging in non-task-oriented dialogue with AI can pose several risks such as privacy breaches, manipulation of personal information, and potential biases based on the data used to train the model. It is important to be aware of these risks and take necessary precautions when interacting with AI systems. |
| All GPT models are created equal and have the same level of risk associated with them. | Different GPT models may have varying levels of risk depending on factors such as their training data sources, algorithms used, and intended use cases. It is important to thoroughly evaluate each model before using it for any application or interaction. |
| Bias can be completely eliminated from GPT models through careful training techniques. | While efforts can be made to reduce bias in GPT models during training by using diverse datasets and removing biased language patterns, complete elimination of bias may not always be possible due to inherent biases present within society itself that influence language usage patterns. |
| The dangers associated with non-task-oriented dialogue only affect individuals who frequently interact with AI systems. | The risks associated with engaging in non-task-oriented dialogue apply to all individuals who interact with AI systems regardless of frequency or expertise level. It is crucial for everyone interacting with these systems – whether occasionally or regularly -to understand potential risks involved so they can make informed decisions about how they choose to engage. |