In the rapidly evolving field of artificial intelligence, the ability to generate insightful and contextually relevant answers has become a cornerstone of modern AI systems. However, a recurring limitation often emerges: when posed with a specific question, many AI systems tend to provide a broad range of potential answers without committing to a concrete recommendation. For instance, when asked for advice on where to spend summer holidays, like in the following example:
an AI might generate a list of plausible destinations but fall short of delivering a decisive, tailored suggestion. A typical answer might look like the following:
While this approach ensures a degree of inclusivity and flexibility, it may not always align with users’ needs, especially in scenarios demanding focused, actionable recommendations.
This gap becomes even more apparent in use cases where clarity and precision are paramount. Users seeking definitive guidance can find themselves sifting through generalized options, which, while accurate, lack the specificity to fully address their requirements. This challenge underscores the need for an approach that not only organizes information systematically but also synthesizes it into a form that directly supports decision-making.
Our AI-driven implementation of the Question-Option-Criteria (QOC) framework is designed to address this precise challenge. The QOC framework, traditionally a tool for structured decision analysis, lends itself naturally to this problem space. By systematically evaluating possible options against a set of defined criteria, the framework enables AI to move beyond merely listing possibilities to providing well-reasoned, concrete suggestions tailored to the user’s context. This approach empowers users to make informed decisions with confidence, bridging the gap between expansive AI-generated possibilities and actionable insights.
What is QOC?
The Question-Option-Criteria (QOC) framework is a structured methodology originally rooted in the field of user interface and user experience (UI/UX) design. It was initially developed to support design decisions by systematically analyzing different options against a set of criteria to answer specific questions. Over time, its utility has expanded far beyond its origins, finding a prominent place in general decision support, particularly in scenarios involving group decision-making. The inherent flexibility and distributive nature of the QOC process make it highly effective in both small and large group contexts.
At its core, the QOC approach involves a sequence of clearly defined steps:
- Identifying Potential Options: The process begins by outlining possible answers or solutions to the given question. These options represent the various pathways that could be considered in the decision-making process.
- Identifying Relevant Criteria: Next, the criteria that are critical for evaluating the options are determined. These criteria serve as the benchmarks against which the options are assessed, ensuring that the decision aligns with the underlying priorities and objectives.
- Evaluating Criteria Importance: Each criterion is then assessed to determine its relative importance in the specific context of the decision. This ensures that the decision-making process is weighted appropriately, reflecting the priorities of the stakeholders involved.
- Evaluating Options: Finally, each option is evaluated based on how well it supports or fulfills the identified criteria. This evaluation provides a clear picture of the strengths and weaknesses of each potential choice.
- Calculating the Preferred Option: In the final step, a preferred option is calculated using a defined formula that synthesizes the evaluations of both the criteria and the options. This formula ensures that the decision is data-driven and reflects the combined input of all evaluations.
The QOC framework is particularly powerful in distributed scenarios where multiple stakeholders are involved. In such cases, each stakeholder can contribute by suggesting their own options and criteria. These contributions are then consolidated to avoid redundancy — for instance, when different stakeholders suggest the same option or criterion but use slightly different wording. Once consolidated, all stakeholders participate in evaluating the options and criteria, as described above. The final preferred option is calculated using the same structured approach, but now incorporates the diverse perspectives and priorities of the group.
Beyond its effectiveness in facilitating collaborative decision-making, the QOC framework offers an additional advantage: it inherently documents the decision process. By systematically recording the options, criteria, evaluations, and final outcomes, the QOC approach ensures transparency and accountability. When supported by appropriate technical tools, this documentation can serve as a valuable resource for understanding the rationale behind decisions, enabling teams to revisit and refine their processes over time.
BlockAI’s implementation of the QOC approach
Our AI-based implementation of the QOC framework leverages intelligent agents to streamline and enhance the decision-making process. The system operates by dynamically instantiating relevant stakeholders as agents within the system to address the given question. This allows for a flexible and comprehensive approach to decision-making that reflects the needs and priorities of all involved parties.
Contextual Stakeholder Identification
To ensure that the most relevant stakeholders are represented, the system enables the user to provide additional context about the question. This contextual information helps the system identify the appropriate stakeholders, ensuring that the decision process is informed by the right perspectives. By incorporating this layer of customization, the system is well-suited to handle a wide variety of decision-making scenarios.
Stakeholder Contributions
Once the stakeholders are instantiated as agents, they engage in the decision-making process by:
- Suggesting Options and Criteria: Each agent proposes potential options to answer the given question, as well as criteria for evaluating those options. These contributions reflect the unique priorities and expertise of the stakeholders.
- Automatic Consolidation: The system consolidates the suggested options and criteria, addressing potential redundancies. For instance, when different agents suggest the same option or criterion but express it differently, the system merges them into unified entities. This ensures clarity and avoids duplication, laying the groundwork for a streamlined evaluation process.
Evaluation and Calculation
After the consolidation step, the agents evaluate both the importance of the criteria and the extent to which each option supports these criteria. This dual evaluation is then processed by the system to calculate the preferred option using a predefined formula. The formula integrates the weighted importance of the criteria with the performance of the options, producing a clear and actionable recommendation.
Example Output
As an example, consider the decision-making scenario outlined in the introduction: selecting a summer holiday destination. After instantiating relevant agents, gathering suggestions, consolidating inputs, and performing evaluations, the system might present a result such as this:
The system’s output in this example provides a clear recommendation based on the consolidated inputs and evaluations.
This AI-driven approach amplifies the effectiveness of the QOC framework by automating and distributing key elements of the process. It enables a scalable, collaborative, and thoroughly documented method for tackling complex decisions across diverse contexts.
Statistical analysis of the results and our contribution to explainable AI
One of the key challenges in the field of artificial intelligence is the lack of transparency in how modern AI algorithms, such as neural networks, arrive at their results. While these algorithms are powerful and capable of solving complex problems, their inner workings often function as a “black box,” obscuring the reasoning and processes that lead to a particular outcome. This opacity creates significant barriers for trust, accountability, and adoption.
The issue of explainability becomes even more pronounced as AI systems are increasingly used in collaborative and high-stakes decision-making scenarios. Without clear insight into why an AI system has made a certain recommendation, users may struggle to assess the reliability and relevance of the outcome, making it difficult to integrate AI into processes that demand human oversight and validation. Addressing this challenge is central to advancing the field of explainable AI (XAI), which aims to develop systems and methodologies that provide clear, interpretable, and actionable insights into AI decision-making processes.
Our work contributes to this critical area by leveraging the structured nature of the QOC framework. By systematically organizing decisions through well-defined questions, options, and criteria, our approach inherently promotes transparency. The detailed evaluations and documented decision-making process ensure that every recommendation made by our AI-driven system can be traced back to its origins, offering users a clear understanding of the “why” behind each outcome. This alignment between structured decision analysis and AI-driven processes represents a meaningful step toward more explainable and trustworthy AI systems.
The architecture of our QOC application is designed to seamlessly integrate the structured QOC framework with the advanced capabilities of large language models (LLMs). At the core of the system lies a dedicated QOC layer, on top of our LLM layer, that orchestrates the entire agent-based QOC process, as described in earlier sections. This layer acts as the decision-making engine, ensuring the systematic and transparent handling of questions, options, and criteria, while leveraging the natural language understanding and generation capabilities of LLMs.
This layered approach ensures that the decision-making process is both systematic and enriched by the contextual and linguistic strengths of LLMs. By combining structured decision analysis with the flexibility and depth of language models, our architecture provides a robust foundation for collaborative, transparent, and effective decision support.
Balancing Explainability Across Layers
In our architecture, the interplay between the LLM layer and the QOC layer highlights a critical aspect of explainable AI. The LLM layer, responsible for generating initial input for the QOC model, remains inherently unexplainable. This lack of transparency stems from the nature of neural networks, where the reasoning behind generated outputs is not directly interpretable. In contrast, the QOC layer, which processes the LLM-generated input to determine concrete answers, provides a significant step forward in explainability.
The QOC layer ensures that results are transparent and traceable. It allows for statistical analysis, enabling insights into the decision-making process. Moreover, it facilitates the identification of critical criteria that influence decisions and supports the calculation of similarly good options, which are alternatives that are not significantly worse than the recommended choice. These features make the QOC layer a cornerstone in advancing toward a more explainable AI system.
The broader challenge lies in reducing reliance on the unexplainable LLM layer and expanding the role of the explainable QOC layer. Ideally, the decision-making process would rely exclusively on explainable components, ensuring complete transparency and accountability. While this goal has not yet been fully realized, it represents an important direction for future research, aiming to bridge the gap between powerful AI capabilities and the need for trust and understanding in AI-driven decisions.
Examples for statistical evaluations of the results
Users are not only empowered with a definitive answer, as described in the last section, but can also trace the rationale behind the recommendation, ensuring transparency and confidence in the decision-making process.
For example, the results of the discussion can be represented visually through a graph.
This graph can illustrate the evaluated options alongside their scores relative to the identified criteria, providing a clear view of how each option performs. Such visualizations enhance the accessibility of the decision-making process, enabling users to grasp the reasoning behind the recommendation quickly. Moreover, the graph can highlight similarly good options, showcasing viable alternatives and reinforcing the robustness and flexibility of the decision. This approach ensures that users not only understand the recommendation but also feel confident in exploring other possibilities if needed.
Additionally, the system can provide a detailed overview of the agents that participated in the decision-making process. This includes listing their names alongside a description of their corresponding roles, offering users clarity on the contributors and their perspectives.
By identifying the agents involved, the system ensures that the decision process is transparent and accountable. This information allows users to understand the diversity of inputs and viewpoints that shaped the recommendation, reinforcing trust in the process while enabling a deeper appreciation of the collaborative effort behind the final result.
We can also include an analysis of the consolidated options and criteria in relation to the full set of options and criteria initially suggested by the agents.
This analysis highlights how individual contributions were merged into the final decision framework, providing insights into the inclusiveness and comprehensiveness of the process. By comparing the consolidated outcomes to the original suggestions, users can trace how overlaps, redundancies, and unique perspectives were handled. This fosters an understanding of the system’s ability to synthesize diverse inputs effectively and ensures that all relevant aspects were considered in the decision-making process.
Additionally, the system can perform an analysis of the consistency in the evaluations provided by the agents for both the criteria and the options. This can be achieved by calculating statistical measures such as the standard deviation of the evaluations.
A low standard deviation would indicate a high level of agreement among the agents, while a higher standard deviation may reveal differing perspectives or priorities. Such an analysis helps identify areas where consensus was strong and where discrepancies may require further attention or discussion. This evaluation consistency check enhances the transparency of the process and provides valuable insights into the alignment and diversity of stakeholder inputs.
We can identify critical criteria by analyzing their importance in relation to their standard deviation. A high standard deviation, indicating a diverse range of opinions among the agents, combined with high importance suggests that the criterion is highly debated and requires careful consideration. On the other hand, criteria with low standard deviation (indicating consistent agreement among agents) and low importance are likely less critical to the decision-making process. Visualizing this relationship on an x-y graph, with importance on one axis and standard deviation on the other, provides a clear representation: the most critical criteria appear in the upper right corner of the graph, while the less critical criteria are positioned in the lower left corner.
This approach highlights where attention should be focused during the evaluation process, facilitating informed and balanced decision-making.
Last but not least, we can identify options that are similarly good to the preferred option, defined as those that are not statistically significantly worse. To determine this, we calculate the standard deviation of the results for each option. Then, we identify options whose scores are within one standard deviation of the winning option. These options fall within a range where their performance is considered statistically indistinguishable from the top choice.
By highlighting these similarly good options, the system provides users with a broader perspective on viable alternatives, enabling them to consider multiple paths forward without sacrificing quality or alignment with the decision criteria.
BAI Team
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