Few-shot and zero-shot learning introduce ethical challenges primarily tied to bias amplification, transparency gaps, and risks of misuse. These approaches enable models to perform tasks with minimal or no labeled examples, often relying on pre-trained knowledge. While useful, their efficiency comes with trade-offs that developers must address to avoid unintended harm.
First, bias amplification is a critical concern. Models trained on large datasets for general tasks can inherit societal biases, which become harder to detect when applied to new tasks with limited data. For example, a zero-shot text classifier might associate “nurse” with “female” or “engineer” with “male” due to patterns in its pre-training data, even if the downstream task wasn’t explicitly designed to reflect those biases. Similarly, a few-shot image recognition system trained on a small set of medical images from one demographic could misdiagnose underrepresented groups. Since there’s little task-specific data to correct these issues, biases in the base model propagate more easily, requiring careful auditing of pre-training data and outputs.
Second, transparency and accountability suffer in these setups. Zero-shot models often rely on abstract reasoning (e.g., matching text prompts to outputs), making it difficult to trace why a specific decision was made. For instance, if a zero-shot hiring tool rejects a candidate based on vague criteria inferred from a job description, explaining the rationale becomes nearly impossible. Few-shot systems also face this issue: with limited training examples, the model’s behavior may depend unpredictably on minor variations in the input data. Developers might struggle to debug errors or defend decisions in high-stakes domains like healthcare or finance, where explainability is legally or ethically mandated.
Finally, misuse and over-reliance pose risks. The low data requirements of these methods make it easier to deploy models in sensitive contexts without rigorous validation. A few-shot model for diagnosing rare diseases, for example, might be adopted prematurely due to its apparent adaptability, leading to harmful errors. Zero-shot systems could also be weaponized for generating misinformation at scale, as they require no task-specific fine-tuning to produce plausible-sounding text or images. Additionally, users might overestimate the robustness of these models, assuming they generalize perfectly to unseen scenarios. Without clear documentation of limitations, developers risk enabling harmful applications or eroding trust in AI systems.
To mitigate these issues, developers should prioritize bias testing, invest in explainability tools tailored to low-data regimes, and establish guidelines for responsible deployment in critical domains.