Discover the Surprising Hidden Dangers of Simplified Prompts in AI – Secrets Revealed!
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Understand the concept of simplified prompts | Simplified prompts are prompts that are designed to be easy to understand and use, often with the help of AI. | Misleading suggestions, unintended consequences, algorithmic bias, overreliance on automation, lack of human oversight, privacy concerns, data manipulation risk, black box systems, ethical implications |
| 2 | Recognize the risks of using simplified prompts | Simplified prompts can lead to misleading suggestions and unintended consequences due to algorithmic bias and lack of human oversight. Overreliance on automation can also lead to privacy concerns and data manipulation risk. Black box systems can make it difficult to understand how decisions are made, leading to ethical implications. | Misleading suggestions, unintended consequences, algorithmic bias, overreliance on automation, lack of human oversight, privacy concerns, data manipulation risk, black box systems, ethical implications |
| 3 | Identify the importance of human oversight | Human oversight is crucial in ensuring that AI systems are making ethical and unbiased decisions. Without human oversight, AI systems can perpetuate biases and make decisions that are harmful to individuals or society as a whole. | Lack of human oversight, ethical implications |
| 4 | Understand the need for transparency in AI systems | Transparency in AI systems is important in order to understand how decisions are made and to identify any biases or errors. Black box systems can make it difficult to understand how decisions are made, leading to ethical implications. | Black box systems, ethical implications |
| 5 | Recognize the importance of addressing algorithmic bias | Algorithmic bias can lead to unfair and discriminatory decisions, perpetuating existing biases in society. It is important to address algorithmic bias in order to ensure that AI systems are making ethical and unbiased decisions. | Algorithmic bias, ethical implications |
In summary, the use of simplified prompts in AI systems can lead to various risks, including misleading suggestions, unintended consequences, algorithmic bias, overreliance on automation, lack of human oversight, privacy concerns, data manipulation risk, black box systems, and ethical implications. It is important to address these risks by implementing human oversight, ensuring transparency in AI systems, and addressing algorithmic bias.
Contents
- How can misleading suggestions impact AI systems?
- What are the unintended consequences of relying too heavily on simplified prompts in AI?
- How does algorithmic bias affect the accuracy and fairness of AI systems?
- Why is overreliance on automation a potential danger for AI technology?
- What risks arise from a lack of human oversight in AI decision-making processes?
- How do privacy concerns factor into the use of simplified prompts in AI systems?
- What is data manipulation risk, and how does it relate to simplified prompts in AI technology?
- Why are black box systems problematic for ethical considerations surrounding AI development and implementation?
- What ethical implications should be considered when using simplified prompts in artificial intelligence?
- Common Mistakes And Misconceptions
How can misleading suggestions impact AI systems?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Misleading suggestions can impact AI systems by reinforcing bias in the algorithm. | Bias reinforcement can occur when the AI system is trained on biased data or when the suggestions provided to the system are biased. | Lack of transparency in the training data can lead to the amplification of existing biases. |
| 2 | Faulty decision-making can also result from misleading suggestions. | The AI system may make incorrect decisions based on the suggestions provided, leading to unintended consequences. | Overreliance on automation can lead to human error in programming, resulting in false positives or negatives. |
| 3 | Unintended consequences can arise from misleading suggestions, such as algorithmic discrimination. | The AI system may discriminate against certain groups based on the biased suggestions provided, leading to negative impacts on marginalized communities. | Manipulation of training data can also lead to algorithmic discrimination. |
| 4 | Adversarial attacks on AI can exploit misleading suggestions to manipulate the system. | Attackers can provide misleading suggestions to the AI system to cause it to make incorrect decisions or take harmful actions. | Lack of robustness in the AI system can make it vulnerable to adversarial attacks. |
| 5 | The ethical implications of AI must be considered when dealing with misleading suggestions. | The use of biased suggestions can have negative impacts on society and raise ethical concerns. | Unforeseen outcomes can occur when using AI systems, and the potential risks must be carefully managed. |
What are the unintended consequences of relying too heavily on simplified prompts in AI?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Lack of context awareness | Simplified prompts in AI may lack context awareness, leading to incorrect or incomplete responses. | Users may receive inaccurate information or make poor decisions based on incomplete or incorrect data. |
| 2 | Overreliance on automation | Relying too heavily on simplified prompts in AI can lead to overreliance on automation, reducing human oversight and increasing vulnerability to attacks. | Users may become complacent and trust the AI system too much, leading to a false sense of security. |
| 3 | Incomplete data analysis | Simplified prompts may not provide enough data for accurate analysis, leading to poor decision-making outcomes. | Users may make decisions based on incomplete or inaccurate data, leading to negative consequences. |
| 4 | Insufficient training data | AI systems may not have enough training data to accurately respond to simplified prompts, leading to limited language understanding. | Users may become frustrated with the AI system’s inability to understand their requests, leading to distrust and decreased usage. |
| 5 | Limited language understanding | Simplified prompts may not provide enough information for AI systems to fully understand the user’s intent, leading to unforeseen edge cases. | Users may encounter unexpected responses or errors, leading to frustration and distrust. |
| 6 | False sense of security | Overreliance on simplified prompts in AI can lead to a false sense of security, as users may assume the system is infallible. | Users may not take appropriate precautions or double-check information, leading to negative consequences. |
| 7 | Unforeseen edge cases | Simplified prompts may not account for all possible scenarios, leading to unforeseen edge cases. | Users may encounter unexpected responses or errors, leading to frustration and distrust. |
| 8 | User frustration and distrust | Inaccurate or incomplete responses from AI systems can lead to user frustration and distrust. | Users may stop using the AI system or seek alternative solutions, leading to decreased usage and revenue. |
| 9 | Poor decision-making outcomes | Inaccurate or incomplete data analysis from AI systems can lead to poor decision-making outcomes. | Users may make decisions based on incomplete or inaccurate data, leading to negative consequences. |
| 10 | Reduced human oversight | Overreliance on automation in AI systems can lead to reduced human oversight, increasing vulnerability to attacks. | Hackers or malicious actors may exploit vulnerabilities in the AI system, leading to data breaches or other negative consequences. |
| 11 | Difficulty in error correction | Simplified prompts may make it difficult to correct errors in AI systems, leading to continued inaccuracies. | Users may become frustrated with the AI system’s inability to correct errors, leading to distrust and decreased usage. |
| 12 | Increased vulnerability to attacks | Overreliance on automation in AI systems can increase vulnerability to attacks, as hackers may exploit vulnerabilities in the system. | Data breaches or other negative consequences may occur, leading to loss of revenue and damage to reputation. |
| 13 | Lack of transparency in algorithms | Lack of transparency in AI algorithms can lead to distrust and decreased usage. | Users may not understand how the AI system works or how decisions are made, leading to decreased trust and usage. |
| 14 | Unintended consequences | Relying too heavily on simplified prompts in AI can lead to unintended consequences, such as inaccurate or incomplete responses. | Users may encounter unexpected responses or errors, leading to frustration and distrust. |
How does algorithmic bias affect the accuracy and fairness of AI systems?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Discrimination in AI systems | AI systems can discriminate against certain groups of people due to biased data sets and limited perspectives considered. | Discrimination can lead to inaccurate predictions and reinforce stereotypes, which can have a negative impact on marginalized groups. |
| 2 | Prejudiced data sets | Prejudiced data sets can perpetuate societal biases and limit the diversity of perspectives considered in AI systems. | Prejudiced data sets can lead to inaccurate predictions and reinforce stereotypes, which can have a negative impact on marginalized groups. |
| 3 | Inaccurate predictions | Inaccurate predictions can result from biased data sets and limited perspectives considered in AI systems. | Inaccurate predictions can lead to unfair treatment of individuals and reinforce stereotypes, which can have a negative impact on marginalized groups. |
| 4 | Lack of diversity | Lack of diversity in the perspectives considered in AI systems can lead to biased outcomes. | Lack of diversity can lead to inaccurate predictions and reinforce stereotypes, which can have a negative impact on marginalized groups. |
| 5 | Marginalized groups affected | Marginalized groups are often the most affected by biased AI systems. | Biased AI systems can perpetuate discrimination and reinforce stereotypes, which can have a negative impact on marginalized groups. |
| 6 | Reinforcement of stereotypes | Biased AI systems can reinforce stereotypes and perpetuate discrimination. | Reinforcement of stereotypes can lead to inaccurate predictions and unfair treatment of individuals, which can have a negative impact on marginalized groups. |
| 7 | Limited perspectives considered | Limited perspectives considered in AI systems can lead to biased outcomes. | Limited perspectives can lead to inaccurate predictions and reinforce stereotypes, which can have a negative impact on marginalized groups. |
| 8 | Amplification of societal biases | Biased AI systems can amplify societal biases and perpetuate discrimination. | Amplification of societal biases can lead to inaccurate predictions and unfair treatment of individuals, which can have a negative impact on marginalized groups. |
| 9 | Ethical concerns raised | Biased AI systems raise ethical concerns about fairness and discrimination. | Ethical concerns can lead to mistrust of AI systems and negative impacts on marginalized groups. |
| 10 | Human oversight necessary | Human oversight is necessary to ensure that AI systems are fair and unbiased. | Lack of human oversight can lead to biased outcomes and negative impacts on marginalized groups. |
| 11 | Transparency and accountability needed | Transparency and accountability are needed to ensure that AI systems are fair and unbiased. | Lack of transparency and accountability can lead to biased outcomes and negative impacts on marginalized groups. |
| 12 | Impact on decision-making processes | Biased AI systems can impact decision-making processes and lead to unfair treatment of individuals. | Impact on decision-making processes can lead to negative impacts on marginalized groups and perpetuate discrimination. |
| 13 | Unintended consequences possible | Biased AI systems can have unintended consequences that negatively impact individuals and groups. | Unintended consequences can lead to negative impacts on marginalized groups and perpetuate discrimination. |
| 14 | Need for ongoing evaluation | Ongoing evaluation is necessary to ensure that AI systems remain fair and unbiased. | Lack of ongoing evaluation can lead to biased outcomes and negative impacts on marginalized groups. |
Why is overreliance on automation a potential danger for AI technology?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Lack of Human Oversight | Overreliance on automation can lead to a lack of human oversight, which can result in unintended consequences. | Unforeseen Consequences |
| 2 | Limited Contextual Understanding | AI technology may have limited contextual understanding, which can lead to incorrect decisions. | Inability to Adapt |
| 3 | Dependence on Data Quality | AI technology is dependent on data quality, and if the data is biased or incomplete, it can lead to algorithmic discrimination. | Algorithmic Discrimination |
| 4 | Ethical Concerns | Overreliance on automation can raise ethical concerns, such as the use of AI for surveillance or the potential loss of privacy. | Ethical Concerns, Data Privacy Issues |
| 5 | Reduced Creativity and Innovation | Overreliance on automation can lead to reduced creativity and innovation, as AI technology may not be able to generate new ideas or solutions. | Reduced Creativity and Innovation, Decreased Critical Thinking Skills |
| 6 | Loss of Jobs and Skills | Overreliance on automation can lead to the loss of jobs and skills, as AI technology may replace human workers. | Loss of Jobs and Skills |
| 7 | Cybersecurity Risks | Overreliance on automation can increase cybersecurity risks, as AI technology may be vulnerable to hacking or other cyber attacks. | Cybersecurity Risks |
| 8 | Overfitting | Overreliance on automation can lead to overfitting, where the AI technology is too closely tailored to the training data and may not perform well on new data. | Overfitting |
| 9 | Lack of Transparency | Overreliance on automation can lead to a lack of transparency, where it may be difficult to understand how the AI technology is making decisions. | Lack of Transparency |
What risks arise from a lack of human oversight in AI decision-making processes?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Lack of human oversight in AI decision-making processes can lead to unintended consequences. | AI systems may make decisions that have negative consequences that were not anticipated by their creators. | Unintended consequences possible |
| 2 | AI decision-making processes may be biased due to the data they are trained on. | AI systems may make decisions that are unfair or discriminatory towards certain groups of people. | Bias in AI decisions |
| 3 | Ethical concerns may arise when AI systems make decisions without human oversight. | AI systems may make decisions that are not aligned with ethical principles or values. | Ethical concerns arise |
| 4 | Accountability issues may be present when AI systems make decisions without human oversight. | It may be difficult to determine who is responsible for the decisions made by AI systems. | Accountability issues present |
| 5 | Transparency may be lacking in AI decision-making processes without human oversight. | It may be difficult to understand how AI systems arrive at their decisions. | Transparency may be lacking |
| 6 | The potential for errors increases when AI systems make decisions without human oversight. | AI systems may make mistakes that could have been caught by a human. | Potential for errors increases |
| 7 | AI systems may have a limited ability to adapt to new situations without human oversight. | AI systems may not be able to handle situations that they were not specifically trained for. | Limited ability to adapt |
| 8 | Overreliance on technology is possible when AI systems make decisions without human oversight. | People may become too reliant on AI systems and not question their decisions. | Overreliance on technology possible |
| 9 | AI systems may be unable to consider context when making decisions without human oversight. | AI systems may not be able to take into account the unique circumstances of a situation. | Inability to consider context |
| 10 | Difficulty in identifying errors may arise when AI systems make decisions without human oversight. | It may be difficult to determine when an AI system has made a mistake. | Difficulty in identifying errors |
| 11 | The legal implications of AI decision-making without human oversight may be unclear. | It may be unclear who is responsible for the decisions made by AI systems and whether they are legally binding. | Legal implications unclear |
| 12 | The trustworthiness of AI systems may be questioned when they make decisions without human oversight. | People may be less likely to trust AI systems if they do not understand how they arrive at their decisions. | Trustworthiness questioned |
| 13 | AI systems may lack empathy and intuition when making decisions without human oversight. | AI systems may not be able to take into account the emotional or intuitive aspects of a situation. | Lack of empathy and intuition |
| 14 | The potential for job displacement may arise when AI systems make decisions without human oversight. | AI systems may be able to perform tasks that were previously done by humans, leading to job loss. | Potential for job displacement |
How do privacy concerns factor into the use of simplified prompts in AI systems?
What is data manipulation risk, and how does it relate to simplified prompts in AI technology?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Define data manipulation risk. | Data manipulation risk refers to the possibility of data being intentionally or unintentionally altered or distorted, leading to inaccurate or biased results. | Incomplete data sets, algorithmic bias, and misleading information can all contribute to data manipulation risk. |
| 2 | Explain how simplified prompts in AI technology can increase data manipulation risk. | Simplified prompts in AI technology can lead to incomplete data sets and biased results. For example, if a prompt only asks for a user’s age, it may not take into account other important factors that could affect the outcome. Additionally, simplified prompts can lead to overreliance on automation and lack of human oversight, which can increase the risk of unintended consequences. | Overreliance on automation, lack of human oversight, and ethical considerations can all contribute to data manipulation risk in AI technology. |
| 3 | Discuss the importance of data quality in mitigating data manipulation risk. | Ensuring data quality is crucial in mitigating data manipulation risk. This includes verifying the accuracy and completeness of data sets, as well as addressing any algorithmic bias or ethical considerations. Machine learning models should also be regularly tested and updated to ensure they are producing accurate and unbiased results. | Data privacy concerns and unforeseen outcomes can also contribute to data manipulation risk, highlighting the importance of ongoing monitoring and risk management. |
Why are black box systems problematic for ethical considerations surrounding AI development and implementation?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Black box systems are problematic for ethical considerations surrounding AI development and implementation because of their inability to explain decisions. | Black box systems are AI models that are opaque and do not provide any insight into how they make decisions. This lack of transparency makes it difficult to understand how the AI arrived at a particular decision, which can lead to mistrust and suspicion. | Limited human oversight, potential for discrimination, risk management challenges, legal liability concerns, public trust issues with AI |
| 2 | Hidden biases in algorithms are another risk factor associated with black box systems. | Algorithms can be biased due to the data they are trained on, which can lead to discriminatory outcomes. However, without transparency, it is difficult to identify and correct these biases. | Inability to explain decisions, potential for discrimination, ethical implications of automation, risk management challenges, legal liability concerns |
| 3 | Unintended consequences of AI are also a concern with black box systems. | Without transparency, it is difficult to anticipate and mitigate unintended consequences of AI, such as job displacement or negative impacts on marginalized communities. | Inability to explain decisions, limited human oversight, ethical implications of automation, risk management challenges, legal liability concerns |
| 4 | Difficulty in accountability is another risk factor associated with black box systems. | Without transparency, it is difficult to hold AI systems accountable for their decisions and actions. This can lead to a lack of responsibility and potential harm to individuals or society as a whole. | Inability to explain decisions, limited human oversight, ethical implications of automation, risk management challenges, legal liability concerns |
| 5 | The potential for discrimination is a significant ethical concern with black box systems. | Without transparency, it is difficult to identify and correct discriminatory outcomes, which can perpetuate existing biases and harm marginalized communities. | Inability to explain decisions, hidden biases in algorithms, ethical implications of automation, risk management challenges, legal liability concerns |
| 6 | Ethical implications of automation are also a concern with black box systems. | Automation can have significant impacts on individuals and society, and without transparency, it is difficult to ensure that these impacts are ethical and aligned with societal values. | Inability to explain decisions, limited human oversight, unintended consequences of AI, risk management challenges, legal liability concerns |
| 7 | Risk management challenges are a significant concern with black box systems. | Without transparency, it is difficult to identify and mitigate risks associated with AI, which can lead to harm to individuals or society as a whole. | Inability to explain decisions, limited human oversight, unintended consequences of AI, ethical implications of automation, legal liability concerns |
| 8 | Legal liability concerns are also a risk factor associated with black box systems. | Without transparency, it is difficult to assign legal liability for harm caused by AI systems, which can lead to a lack of accountability and potential harm to individuals or society as a whole. | Inability to explain decisions, limited human oversight, unintended consequences of AI, ethical implications of automation, risk management challenges |
| 9 | The need for interpretability standards is a potential solution to the risks associated with black box systems. | Interpretability standards would require AI models to provide explanations for their decisions, which would increase transparency and accountability. | Inability to explain decisions, hidden biases in algorithms, ethical implications of automation, risk management challenges, legal liability concerns |
| 10 | Public trust issues with AI are a significant concern with black box systems. | Without transparency, it is difficult to build trust in AI systems, which can lead to a lack of adoption and potential harm to individuals or society as a whole. | Inability to explain decisions, limited human oversight, unintended consequences of AI, ethical implications of automation, risk management challenges, legal liability concerns |
| 11 | The impact on privacy rights is another ethical concern with black box systems. | Without transparency, it is difficult to ensure that AI systems are not violating individuals’ privacy rights, which can lead to harm and mistrust. | Inability to explain decisions, limited human oversight, ethical implications of automation, risk management challenges, legal liability concerns |
| 12 | The technological determinism critique is a potential solution to the risks associated with black box systems. | The technological determinism critique argues that technology is not neutral and that it reflects and reinforces existing power structures and biases. By acknowledging this, developers can work to mitigate these biases and ensure that AI is aligned with societal values. | Inability to explain decisions, hidden biases in algorithms, ethical implications of automation, risk management challenges, legal liability concerns |
| 13 | Ethics and governance frameworks are a potential solution to the risks associated with black box systems. | Ethics and governance frameworks provide guidelines for the development and implementation of AI that are aligned with societal values and ethical principles. | Inability to explain decisions, hidden biases in algorithms, ethical implications of automation, risk management challenges, legal liability concerns |
| 14 | Overall, black box systems are problematic for ethical considerations surrounding AI development and implementation due to their lack of transparency and accountability. | Without transparency, it is difficult to ensure that AI is aligned with societal values and ethical principles, which can lead to harm to individuals or society as a whole. | Inability to explain decisions, hidden biases in algorithms, unintended consequences of AI, difficulty in accountability, potential for discrimination, ethical implications of automation, risk management challenges, legal liability concerns |
What ethical implications should be considered when using simplified prompts in artificial intelligence?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Consider the lack of transparency in AI when using simplified prompts. | Simplified prompts may not provide enough information for users to understand how AI is making decisions. | Lack of transparency can lead to mistrust and suspicion of AI. |
| 2 | Take responsibility for AI outcomes when using simplified prompts. | Simplified prompts may lead to unintended consequences, and it is important to take responsibility for these outcomes. | Lack of responsibility can lead to harm to individuals or communities. |
| 3 | Address privacy concerns with AI when using simplified prompts. | Simplified prompts may collect and use personal data without informed consent, leading to privacy violations. | Lack of privacy protections can lead to harm to individuals or communities. |
| 4 | Ensure fairness and equity in AI when using simplified prompts. | Simplified prompts may perpetuate biases and discrimination, leading to unfair outcomes for certain groups. | Lack of fairness and equity can lead to harm to marginalized communities. |
| 5 | Provide human oversight of AI when using simplified prompts. | Simplified prompts may not be able to account for all possible scenarios, and human oversight can help ensure ethical decision-making. | Lack of human oversight can lead to unintended consequences and harm. |
| 6 | Establish accountability for AI decisions when using simplified prompts. | Simplified prompts may lead to decisions that harm individuals or communities, and it is important to establish accountability for these decisions. | Lack of accountability can lead to harm to individuals or communities. |
| 7 | Consider cultural sensitivity in AI when using simplified prompts. | Simplified prompts may not take into account cultural differences and can lead to harm or offense. | Lack of cultural sensitivity can lead to harm to individuals or communities. |
| 8 | Obtain informed consent with data usage when using simplified prompts. | Simplified prompts may collect and use personal data without the user’s knowledge or consent, leading to privacy violations. | Lack of informed consent can lead to harm to individuals or communities. |
| 9 | Address potential harm from biased algorithms when using simplified prompts. | Simplified prompts may perpetuate biases and discrimination, leading to harm to individuals or communities. | Lack of addressing potential harm can lead to harm to marginalized communities. |
| 10 | Recognize the risk of discrimination through simplified prompts. | Simplified prompts may perpetuate discrimination against certain groups, leading to harm to individuals or communities. | Lack of recognition can lead to harm to marginalized communities. |
| 11 | Consider ethical considerations with machine learning when using simplified prompts. | Simplified prompts may not take into account ethical considerations, leading to harm to individuals or communities. | Lack of ethical considerations can lead to harm to individuals or communities. |
| 12 | Address the impact on marginalized communities when using simplified prompts. | Simplified prompts may perpetuate harm to marginalized communities, leading to further marginalization. | Lack of addressing the impact can lead to harm to marginalized communities. |
| 13 | Ensure the trustworthiness of simplified prompts when using them in AI. | Simplified prompts may not be reliable or accurate, leading to unintended consequences and harm. | Lack of trustworthiness can lead to harm to individuals or communities. |
| 14 | Prevent the misuse or abuse of simplified prompts in AI. | Simplified prompts may be used for malicious purposes, leading to harm to individuals or communities. | Lack of prevention can lead to harm to individuals or communities. |
Common Mistakes And Misconceptions
| Mistake/Misconception | Correct Viewpoint |
|---|---|
| AI is unbiased and always makes the right decision. | AI systems are only as unbiased as their training data, which can contain biases and inaccuracies. Additionally, AI systems may not always make the correct decision due to limitations in their programming or unforeseen circumstances. It is important to continuously monitor and evaluate AI systems for potential biases and errors. |
| Simplified prompts lead to more efficient and accurate results from AI systems. | While simplified prompts may increase efficiency, they can also limit the scope of information that an AI system considers when making decisions. This can result in oversimplification or incomplete analysis of complex situations, leading to inaccurate or biased outcomes. More detailed prompts may be necessary for a comprehensive understanding of a situation before making decisions based on AI recommendations. |
| The use of simplified prompts eliminates the need for human oversight in decision-making processes involving AI systems. | Human oversight is crucial in ensuring that an organization’s values align with those reflected by its use of technology such as artificial intelligence (AI). Humans must ensure that ethical considerations are taken into account when designing algorithms used by these technologies so that they do not perpetuate existing social inequalities or cause harm unintentionally. |
| The benefits of using simplified prompts outweigh any potential risks associated with them. | While there may be some benefits associated with using simplified prompts, it is essential to consider all possible risks involved before implementing them fully into an organization’s workflow process involving artificial intelligence (AI) technology. These risks include but are not limited to: increased bias towards certain groups; decreased accuracy due to oversimplification; lack of transparency regarding how decisions were made; etcetera. |