How should I verify what AI tells me?
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To verify what AI tells you, there are several methods available. Verification and validation (V&V) is one approach used by software analysts to gain trust in AI systems . However, AI systems have unique features that make them challenging to check using conventional V&V methods. Nevertheless, alternative methods exist that enable the V&V of AI software . Additionally, trustworthiness is a crucial aspect of AI systems. Ensuring the trustworthiness of AI systems involves understanding what it means for an AI system to be trustworthy and taking actions to achieve this goal . Trust-based consent processes can also be used as an alternative to traditional consent mechanisms, where decisions to engage with AI systems are based on ongoing negotiation between stakeholders . Finally, a "trust but verify continuously" approach can be employed, which includes AI assurance activities throughout the system development lifecycle .
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| Papers (5) | Insight |
|---|---|
| The paper discusses the concept of "trust but verify continuously" for AI assurance, which includes activities throughout the system development lifecycle to establish trust in AI systems. | |
| The paper discusses the challenges of verifying AI technologies and proposes a trust-based alternative to consent. | |
| AI systems can be verified and validated using alternative methods that enable trust in their functionality and reliability. | |
2 Citations | The paper discusses the challenges of verifying AI systems and proposes a trust model and set of properties to ensure their trustworthiness. |
23 Jun 2022 | The paper discusses the challenges of verifying AI systems and suggests a trust model and set of properties to ensure their trustworthiness. |
Related Questions
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Why are AI information often incorrect?5 answersAI information is often incorrect due to various factors highlighted in the research. One key reason is the potential for incorrect AI results to impact human decision-making, leading to errors in tasks such as radiologist interpretations. Additionally, AI systems can be deceptive, providing misleading explanations that make them appear more reliable than they actually are, which can lead to misinformation spread in disinformation campaigns. Moreover, biases and false information can be transmitted from generative AI models to users, contributing to the misconception that these models surpass human-level reasoning. Furthermore, even perfectly trained AI applications can produce faulty outputs if fed with erroneous inputs, emphasizing the importance of high-quality data and error-correction mechanisms in AI implementation.
How can AI chatbots be made more accurate and unbiased?4 answersAI chatbots can be made more accurate and unbiased by implementing several strategies. Firstly, it is important to identify and address the biases present in the training data used for these chatbots. This can be done by carefully engineering prompts and measuring the bias in the generated responses. Additionally, it is crucial to improve the sensitivity of chatbots to changes in prompts, as small changes can lead to different levels of fairness. Implementing "corrections" or mitigation strategies can help improve the fairness of these systems. Furthermore, an impartial review panel with access to model parameters can measure different types of biases and recommend safeguards to reduce discrimination and improve accuracy. By incorporating these strategies, AI chatbots can become more accurate and less biased in their responses.
How do the current tools used to check ai generated text accuracy work?4 answersCurrent tools used to check the accuracy of AI-generated text work by evaluating the ability of these tools to differentiate between human-written text and AI-generated text. The tools are assessed based on accuracy and error type analysis. The research covers both publicly available tools and commercial systems widely used in the academic setting. The findings indicate that the available detection tools are neither accurate nor reliable, with a bias towards classifying the output as human-written rather than detecting AI-generated text. Additionally, content obfuscation techniques significantly worsen the performance of these tools. The evaluation of code generators for AI-based programs also faces challenges, with the current practice relying on output similarity metrics. However, it is unclear which metric is most suitable for specific contexts. Human evaluation is used to compare the estimates from automatic metrics and provide practical insights into their strengths and limitations.
Are users able to accurately detect AI system misinformation?5 answersUsers are able to detect AI system misinformation to some extent. Previous research has shown that AI explanations help people determine the veracity of information online and change their beliefs. However, little is known about how susceptible people are to deceptive AI systems. With the increasing prevalence of large language models like GPT-3 that can generate highly believable and deceptive explanations, it becomes important to understand how AI systems with honest and deceptive explanations affect people's ability to discern true news from fake news online. Further research is needed to investigate this aspect and determine the extent to which users can accurately detect AI system misinformation.
How should I verify AI responses to questions?5 answersVerifying AI responses to questions involves a multi-faceted approach. One methodology is to analyze the quality of the AI's responses through cross-sectional studies, as demonstrated by Ayers et al.. Another approach is to utilize the Turing Test, which has been considered a reliable test for AI by Shapiro. Additionally, Hill et al. propose embedding AI plans into a dependently-typed language, Agda, to enable users to reason about and verify more abstract properties of plans. Dailard discusses the challenges of implementing contraceptive coverage laws in the private sector, highlighting the need for exemptions based on religious grounds. Lastly, Mengqiu presents a verification method for AI calculation results, involving the comparison of reference and calculated digest values. These diverse approaches provide insights into the verification of AI responses to questions.