Semantics of the Black-Box: Using knowledge-infused learning approach to make AI systems more interpretable and explainable

Editor's Notes

• #4: Slide 3: Inner circle : talks about our research areas and strength
• #12: A nice knowledge graph, which is a knowledge graphs ----- picture over here One side ---> Empathi ezDI image → other side When an agent communicate with humans, Empathy, policies, trustworthy → inform the behavior of the agent
• #14: ---- Slide before PAC Learning ----- Explaining one of the them -- why knowlege graph would help There are many NLP challenges, why knowledge Graph would work GPT-3 --- issues Can KG solve it How to get a better context for effective output
• #15: You can have relationship between concepts
• #20: Video : When the slide will be uploaded.
• #21: a(i) Domain knowledge of traffic in the form of concepts and relationships (mostly causal) from the ConceptNet a(ii) Probabilistic Graphical Model (PGM) that explains the conditional dependencies between variables in traffic domain is enriched by adding the missing random variables, links, and link directions extracted from ConceptNet b : Shows how this enriched PGM is used to correlate contextually related data of different modalities. 3 sources of knowledge (Geo-Spatially and temporally) [ We need to put this in the slide] → Open Street Map → Smart City Knowledge Graph → [Find the third one]
• #22: ---- We should have another insight: --- it is still a coarse-grained use of kG for making sense of clusters --- We need to provide an example in a more detailed: There is an explicit relationship between two concept ---- A person owns a company or works for a company Both Example Kaushik: points Manas: points (enriching the embedding)
• #24: What is knowledge infusion in deep learning? Using knowledge to change input (shallow), to change parameters (semi-deep), to change parameters by mapping to a stratified hierarchy (Deep) (Ex: 1st layer knowledge x, 2nd layer knowledge y, etc). Can use diagram from pydata Berlin talk.
• #29: → Integration of Knowledge Representation with Statistical Representation of Text is also straightforward → Devoid of Semantic Representation → Shallow merging needs to be demonstrated
• #30: Possible proposal material
• #33: In semi-deep infusion paradigm, the learning system of the model is altered either through a probabilistic threshold (e.g. attention or constraints) or data redundancy for gains in performance. There are three broad categories of SEMI-DEEP Infusion: Forcing methods: the prediction of the model from the learnt representation is improved by mixing (sigmoidal, concatenation, multiplication) the representation of input as ground truth to enrich latent representation. Attention methods: These methods improves upon the forcing methods by making the model capable of selecting parts of the learnt representations that needs to be modified. Knowledge-base methods: Since both forcing and attention methods rely on the input data which is a poor manifestation of the real world, thus models suffer from problems such as exposure bias. The knowledge-base methods replace the dependency of the model from input text to knowledge-base for attention and forcing. In knowledge-based LSTMs, rather than putting attention on input text, the method used attention as a switch, which when open contextualize the latent representation through representations of relevant concepts in knowledge base. When the switch is off, the latent representation is used as it is. In knowledge-based GANs, the model learn by maximizing the reward, which is generating the representation of the input which matches the output. One way of formulating this reward is minimization of KL divergence. In this architecture, the attention module is influenced by reward function which is a learnable constraint.
• #34: r/BPD: Borderline PErsonality Disorder SBI: Suicide Behavior Ideation
• #37: Correlation matrix is the parameters for the deep learning algorithm for DSM-5
• #38: Method: Semantic encoding - decoding optimization (Pearson Correlation) DD: Correlation between DSM-5 Categories RR: Correlation between concepts in Reddit posts irrespective of the user DR: Correlation between the concepts in Reddit
• #39: Qualitatively, this is the outcome of the semantic encoding and decoding method. You are able to label a post in a subreddit with an appropriate DSM-5 category. On the Left, is all such mapping that the model learnt.
• #42: Why? Shallow - Can help enrich neural representations. Semi-deep: Can help with tweaking parameters to follow correlations present in knowledge (in addition to data) in constructing representations. Deep - Can identify what correlation in the knowledge in addition to data matters in which layer to finally construct a representation that benefits from knowledge infusion at all layers. Ex: Shallow: Wikipedia based GNN training to answer questions - hopefully captures relationships. Semi-deep: Force understanding that Obama is correlated to Michele Obama through relationships like spouse, by explicitly modifying the attention (correlation matrix) - definitely captures relationships. Deep: Identify number relationships, how they relate to metrics, how those metrics relate to what is being measured (blood pressure), how blood pressure relates to what is being predicted - definitely captures nested/hierarchical relationship semantics <Example of Deep Knowledge Infusion>
• #44: Definition of Interpretability and Explainability
• #47: Multi-hop Two-hop Changing the post
• #49: Example of explanation: For each time of knowledge infusion Shallow: TSNE clusters can show that KG relationships were captured, sports words come together Semi-Deep: Attention matrix can show if KG relationships were captured, sports words attend to each other with high correlation Deep: Representations at each layer can be visualized through concept maps in the stratified KG. Members of a hierarchical concept lower in the hierarchy correlate highly with those higher in the hierarchy on visualization of concepts from a class hierarchy. (Ex: 30, cistolic pressure, heart attack all would be close as they map to the same hierarchical concept)
• #53: Explainability example in Education
• #54: Current approach assessing the mastery of a student in a course and provide multiple pathways for improving the learning outcomes relies on a predictive algorithm: Bayesian Knowledge Tracing (BKT). The approach assess following tendencies of the student: He knows the answers correctly He guessed the answers correctly What is the improvement after multiple attempts However, it does not tell: How far from the correct answer, is the student’s answer? What relevant concepts the student needs to learn? Also, the algorithm does not provide the capability to assess whether the student has mastered a topic in a course or course itself. On this slide, a student was asked two questions from the topic of “Phosphorylation”. BKT would consider these questions independently, Whereas, Knowledge infusion would find the relation between the two question, through the entity: Phosphorylation Since, the answer don’t match the true answer, BKT would not accept them as correct. The question in the red, could not be answered by BKT, because it does not know the relation between the questions However, if we use knowledge Infusion: It knows the relation between the concepts through Phosphorylation, so, it can answer the question in red. It knows that “adenosine 3-phosphate” is an alias of “Adenosine Triphosphate”, so it would accept the response It would measure the distance between “Glucose Phosphate” and “Glucose Monophosphate” to see: How far from the correct answer is the student’s answer? What new concepts the student needs to learn to achieve mastery on this topic
• #55: The concepts asked in the question are addition of phosphate to glucose and addition of phosphate to adenosine diphosphate From the KG, the relation between the two is obtained as relating to phosphorylation The answer the student provides which is adenosine 3-phosphate might be predicted as wrong by the DN (because it is not adenosine tri-phosphate). The wrong answer triggers search through the KG to figure out how far from the right answer. The explanation adenosine tri-phosphate is an alias of adenosine 3-phosphate and therefore the explanation shows that the student was actually correct and hence has attained explainable mastery. Education knowledge graph can be constructed using the content from MOOC, Coursera, Khan Academy, Udemy, Udacity, Books, epubs from Amazon https://khanacademy.fandom.com/wiki/Knowledge_Map
• #56: Bayesian knowledge tracing not adequate as explanations required to know what other concepts the student might need to attain mastery These concepts can be found in the KG Furthermore, the KG can provide explanation for how far the current level is from mastery.
• #62: Do we need to provide a list of workshop and tutorials conducted