Human-in-the-Loop Safeguards
Description
Human-in-the-loop safeguards establish systematic checkpoints where human experts review, validate, or override AI/ML system decisions before they take effect. This governance approach combines automated efficiency with human judgement by defining clear intervention criteria (such as uncertainty thresholds, risk levels, or sensitive contexts) that trigger mandatory human oversight. By incorporating domain expertise, ethical considerations, and contextual understanding that machines may lack, these safeguards help ensure that critical decisions maintain appropriate human accountability whilst preserving the benefits of automated processing for routine cases.
Example Use Cases
Safety
Implementing mandatory human physician review for any medical AI diagnostic recommendation before treatment decisions are made, especially for complex cases or when the system confidence is below established thresholds, ensuring patient safety through expert oversight.
Transparency
Requiring human review of automated loan approval decisions when applicants request explanations or appeal rejections, allowing human underwriters to provide clear reasoning and ensure customers understand the decision-making process behind their application outcomes.
Fairness
Mandating human oversight when hiring algorithms flag candidates from underrepresented groups for rejection, enabling recruiters to verify that decisions are based on legitimate job-relevant criteria rather than potential algorithmic bias, and providing fair recourse mechanisms.
Limitations
- Scales poorly with high request volumes, creating bottlenecks that can delay critical decisions and potentially overwhelm human reviewers with excessive workload.
- Introduces significant latency into automated processes, potentially making time-sensitive applications impractical or reducing user satisfaction with slower response times.
- Human reviewers may experience decision fatigue, leading to decreased attention quality over time and potential inconsistency in review standards across different cases or time periods.
- Risk of automation bias where humans defer too readily to AI recommendations rather than providing meaningful independent review, undermining the safeguard's effectiveness.
- Requires significant ongoing investment in human resources, training, and expertise maintenance, making it expensive to implement and sustain across large-scale systems.
Resources
Research Papers
Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies
Human-in-the-loop machine learning: a state of the art
Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them.
A Survey of Human-in-the-loop for Machine Learning
Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches. In this paper, we survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. Using the above categorization, we summarize major approaches in the field; along with their technical strengths/ weaknesses, we have simple classification and discussion in natural language processing, computer vision, and others. Besides, we provide some open challenges and opportunities. This survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.