Discover the core dimension of responsible AI that a company should consider when using AI to aggregate customer reviews. Learn how to address concerns about transparency and accuracy while clearly labeling AI involvement. Options include fairness, governance, transparency, and explainability.
Table of Contents
Question
A company is using artificial intelligence (AI) to aggregate individual customer reviews into a product review synopsis. The development team is concerned that the review synopsis will be misleading to customers, and the team wants to clearly label the involvement of AI.
Which core dimension of responsible AI should they consider?
A. Fairness
B. Governance
C. Transparency
D. Explainability
Answer
C. Transparency
Explanation
Transparency regarding AI technologies, development processes, and outcomes signals a commitment to responsible, trustworthy, and responsible AI. It empowers the assessment of systems for bias, helps enable meaningful human oversight, and builds public trust through open communication. A lack of transparency prevents responsible AI development.
Transparency plays a crucial role in responsible AI practices, particularly when AI systems are involved in generating or influencing content that is presented to customers. In this case, the development team wants to address concerns about the potential misleading nature of the review synopsis and ensure that customers are aware of the involvement of AI in the process.
To promote transparency in AI-generated review synopses, the company can take several steps:
- Clear labeling: The company should prominently and explicitly label the involvement of AI in generating the review synopses. This labeling can be displayed alongside the synopsis or in close proximity, ensuring that customers are informed about the AI’s role in the process. Clear labeling helps manage customer expectations and avoids potential misconceptions about the reviews’ origin or reliability.
- Disclosure of methodology: The company should provide a clear explanation of the methodology used by the AI system to aggregate and summarize the customer reviews. This disclosure should outline the key factors considered, any weighting or ranking algorithms employed, and the overall process used to generate the synopsis. By sharing this information, the company enhances transparency and allows customers to understand how the review synopsis is created.
- Data sources and limitations: It is important to disclose the sources of the customer reviews used in the aggregation process. This includes clarifying whether the reviews are collected from a specific platform, timeframe, or demographic. Additionally, the company should communicate any limitations or biases associated with the data sources to ensure customers have a complete understanding of the review synthesis process.
- Regular auditing and monitoring: To maintain transparency, the company should establish processes for auditing and monitoring the AI system’s performance in generating review synopses. This includes periodically reviewing the synthesized outputs to identify and address any potential biases, inaccuracies, or unintended consequences. Auditing and monitoring help maintain the system’s transparency and accuracy over time.
By considering transparency as a core dimension of responsible AI, the development team can address concerns about potential misleading information in review synopses. Clear labeling, disclosure of methodology, data source information, and ongoing monitoring contribute to building trust with customers and ensuring that they are well-informed about the involvement of AI in the review aggregation process.
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