Leveraging Graph Algorithms to Enable Responsible AI Reasoning
Large language models have shown immense promise in their ability to generate remarkably human-like text. However, their natural language generation still suffers from critical issues like factual inaccuracy, logical incoherence, and potential propagation of biases. This undermines trust in AI systems.
Without a grounded understanding of the world, language models too easily hallucinate false knowledge and make unsupported speculative leaps based on dataset biases. Their substantial risk of generating logically incorrect or unethical text makes responsibly deploying them into production difficult.
Fortunately, techniques exist to impart models with more responsible reasoning abilities — specifically by integrating structured knowledge graphs. Knowledge graphs represent factual information and relationships in rich network structures mirroring real-world entanglements.
However, knowledge graphs left uninterpreted fall short of fully informing language models. Simply data dumping graphs into models may exacerbate rather than solve accuracy issues. The true value lies in analyzing the topology to extract key learnings about the interconnected knowledge.
This is where graph algorithms come into play. The entire field of graph analytics focuses on computationally interrogating network structures to uncover non-obvious insights like influential nodes, densely connected modules, optimal paths, and similarities.
Rather than having language models intuitively “rediscover” basic knowledge as they are trained through raw text, graph algorithms can preprocess and surface key patterns, contexts, relationships, and priorities to prime model reasoning.
The knowledge is already there in the graphs — algorithms help extract it responsibly. Let’s explore an example next of augmenting clinical decision support with graphs and better ground an AI assistant’s reasoning made by Google…
Outline :
I: Augmenting Clinical Decision Support
We will use the real-world example of Apollo Hospital leveraging graph algorithms on top of Google’s MedLM model for an AI-assisted clinical decision support system. This grounds the concepts in a healthcare case study.
Transition: Transitioning from the specific healthcare example, we will make some broader connections to the category of centrality algorithms and their value.
II: The Central Role of Centralities
This section will explain centrality algorithms more generally — how scoring entity influence provides powerful reasoning signals for language models regarding credibility and relevance.
Transition: We highlight how centrality focuses on individual entity priorities, leading to the next section on community-level insights.
III: Community Priorities
Here we introduce community detection algorithms in graphs, explaining how they identify spheres of understanding for more contextual reasoning across diverse domains and knowledge bases.
Transition: Pulling together centrality and community detection, we discuss how graph algorithms as a whole enable responsible reasoning by providing structural foundations.
IV: Responsibility Through Structure
In this section, we summarize how algorithms preprocess key graph insights that would be prohibitively difficult for language models to rediscover on their own within raw data. This computational legwork provides scaffolding for reliable reasoning.
Transition: To wrap up, we note that knowledge graphs continuously evolve, making a data-centric mindset important for updating perspectives over time through reprocessing.
V: Adopting a Data-Centric Mindset
We conclude by discussing the need for recurring algorithm implementation to capture emerging knowledge in dynamic graphs, conveying new signals to downstream AI reasoning engines. This enables perpetually realigned reasoning.
I. Augmenting Clinical Decision Support
Doctors today face overwhelming information overload from research literature, patient medical records, lab tests, health databases, etc. Manually piecing together insights is infeasible. AI assistance that could analyze case data and surface the most relevant findings and recommendations to clinicians would be invaluable.
Apollo, a leading hospital group in India, built an AI-based clinical support engine by leveraging Google’s MedLM — a state-of-the-art language model optimized for medical text. However, they recognized raw MedLM still risked generating unsupported speculations or incorrect inferences if unaugmented.
To keep MedLM’s outputs grounded, Apollo constructed a comprehensive clinical knowledge graph containing structured representations of key information from patient records — symptoms, medications, diagnoses, procedures, family history etc. However, raw patient data alone provides insufficient context.
Additional abstractions were needed to prime MedLM’s reasoning. This graph underwent analytics using algorithms like PageRank, which assigned influence scores to medical entities based on their relationships and intersections in the graph.
Personalized PageRank was then run for each patient case to surface the most salient entities specifically relevant to them. This provided MedLM a custom starting context outside the free-text medical notes.
Finally, Apollo optimized MedLM by having it ingest these graph analytics outputs, training it on real-world medical data-driven insights.
This taught MedLM to prioritize critical explanations grounded in key influential medical concepts rather than pursue speculative digressions. The graph algorithms enabled MedLM to reason responsibly.
By outsourcing the extraction of entity influence signals to the graph analytics, MedLM could rely on that computational guidance to focus its explanatory chains. The fusion proved highly accurate in clinical evaluations.
II. The Central Role of Centralities
Influence algorithms like PageRank and Betweenness Centrality are a profoundly useful paradigm for extracting insights from knowledge graphs.
Essentially, these graph algorithms compute metrics that quantify the level of “influence” or “importance” of entities based on the graph connections. Nodes with high scores are highly influential over information flows in that network.
For example, in a citation network of academic papers, influential publications tend to be ones most heavily cited by other reputable papers. PageRank surfaces these computationally.
As such, centralities provide pre-computed indications to language models of which nodes can be considered reputable or proof of concepts within networks. Rather than having models waste computations rediscovering basic influence hierarchies, graph algorithms package this as an informed starting point.
Guiding language models to orient generation around entities with high centrality scores results in stickier, logically cohesive outputs. Chains of reasoning and explanation form anchored to socially proven, credible topics less susceptible to dubious digressions.
Without preprocessed centrality indicators, language models struggle to determine social proof and can spiral into unreliable conjectures. Graph algorithms empower more accountable, evidentially supported generation.
Beyond healthcare, subject hierarchies emerge applying centralities across diverse knowledge graphs — like identifying the most influential authors in literature, pivotal court cases in legal networks, or trending fan communities around entertainment franchises.
Centralities quantify power dynamics to tap into as guidance. The nodes with high centrality scores essentially serve as navigational beacons for reliable, grounded language model reasoning, keeping generation on a tightly guided yet insight-rich trajectory.
III. Community Priorities
While node-level centrality metrics provide local guidance, community detection algorithms surface bigger picture insights — tightly interconnected clusters of entities and relationships representing niches or focused topics.
Algorithms like label propagation identify modules of densely linked nodes, essentially extracting disciplines and spheres of knowledgeability from complex heterogeneous graphs.
In healthcare knowledge graphs, these detected communities may correspond to disease specialty groups and care standards around them. Such computational extraction allows language models to fill knowledge gaps with reasoned speculation specifically tailored to disease domains.
Rather than irresponsibly guessing blindly across medical contexts, community-level directions ground reasoning by narrowing perspectives. Unknowns can be imputed from specialty-specific expectations and best practices.
Community guidance expands beyond healthcare as well. In knowledge graphs encoding humanities or social sciences, community detection uncovers schools of thought, literary movements, theoretical paradigms, etc. with their own customs.
These computationally extracted bubbles inject “common sense” into language models — guiding reasoning strategies by grounding generation in the cultural norms, rhetorical patterns, terminology, and precedent cases associated with in-group preferences of those communities.
This effectively establishes solution spaces for reasoning chains, constraining speculation to stay on target according to sphere-specific beliefs. Reasoning trajectories adhere to community insights rather than unpredictably diverging.
In essence, graph communities provide encapsulated, domain-aware scopes of understanding for language models to safely theorize within rather than hazarding ignorant generalizations. Responsibility emerges from reasoning shaped by extracted groupings.
IV. Responsibility Through Structure
The sheer vastness of knowledge graphs poses computational challenges for language models attempting to infer insightful explanations solely from raw relational data dumps. Without additional structure, models are overwhelmed — left to fumble naively, making blind assumptions and logically unfounded leaps.
This drives irresponsible generation — factual errors, unsupported opinions, and biases stemming from models overextending beyond their evidentiary limitations. Trusted reasoning requires informational scaffolding.
Fortunately, by pre-processing knowledge graphs using graph algorithms, we can save models from their own speculative excesses. Algorithms provide crunched conceptual perspectives — revealing communities, influential nodes, optimal connections, and hidden patterns.
Essentially, algorithms can tackle the analytical heavy-lifting of profiling relationships, priorities, and focus areas within graphs beforehand rather than leaving models to rediscover fundamentals.
The fruits of algorithmic efforts establish structural foundations for reliable language model outputs — extracted groupings constrain scopes, ranked nodes guide traversal orders, projected links bound exploration.
Together this supplies an architectural framework upon which to responsibly construct understandings edge-by-edge rather than hastily overgeneralizing. Reasoning trajectories stay evidentially tethered.
By outsourcing the extraction of key graph insights to efficient algorithms, language models are freed to focus efforts on nuanced textual generation guided by those computational cairns. Bias risks inherently minimize absent aimless meandering.
The knowledge is stored within the graphs, waiting to be uncovered responsibly. Graph algorithms hand models structural compasses to navigate dense knowledge spaces. In tandem, understanding unfolds securely.
V. Adopting a Data-Centric Mindset
When applying graph algorithms to prime AI reasoning, it is crucial we don’t just view graphs as fixed scaffolds. As knowledge representations, graphs evolve — new discoveries arise, relationships change. Without accommodating this flux, models risk reasoning on outdated assumptions.
A data-centric mindset recognizes graphs as dynamic substrates requiring re-processing as underlying data shifts. Rather than one-off analysis, we must revisit graphs continuously as alive systems.
This means implementing graph algorithms like influence scoring and community detection periodically to capture emerging perspectives — refreshing node rankings, redefining cluster contours. Readymade structural guidance expires otherwise.
Processing streams must then convey refreshed insights to downstream AI models — interfacing algorithms directly with reasoning engines as input layers. Only by dynamically coupling graph analytics and consumers can model interpretations stay contemporaneous.
Architecting recursive pipelines allows models to perpetually realign reasoning in sync with the current state of knowledge as represented in graphic substrates. Reality’s patterns keep models grounded in the now rather than the past.
Through data-centricity, we can achieve computational models that don’t just deduce — but persistently re-deduce — as our collective understanding inevitably shifts. Languages permanently reinvented find new ways to responsibly express expanding wisdom. The truth refuses stagnation.
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Neuro-Symbolic is at the heart of the Fribl engine in order to automate HR reasoning with a reliable and fair AI system. We just started with CV screening.
Chief AI Officer & Architect : Builder of Neuro-Symbolic AI Systems @Fribl enhanced GenAI for HR
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