EP4066166A1 - Détection de polarisation et explication de modèles d'apprentissage profond - Google Patents

Détection de polarisation et explication de modèles d'apprentissage profond

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Publication number
EP4066166A1
EP4066166A1 EP20771681.2A EP20771681A EP4066166A1 EP 4066166 A1 EP4066166 A1 EP 4066166A1 EP 20771681 A EP20771681 A EP 20771681A EP 4066166 A1 EP4066166 A1 EP 4066166A1
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EP
European Patent Office
Prior art keywords
bias
data
causal
group
clusters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
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EP20771681.2A
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German (de)
English (en)
Inventor
Janani VENUGOPALAN
Sudipta Pathak
Wei Xia
Sanjeev SRIVASTAVA
Arun Ramamurthy
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Siemens Corp
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Siemens Corp
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Publication date
Application filed by Siemens Corp filed Critical Siemens Corp
Publication of EP4066166A1 publication Critical patent/EP4066166A1/fr
Pending legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation

Definitions

  • This application relates to deep learning models. More particularly, this application relates to a system that infers latent group bias from deep learning models of human decisions for improved explainability of the deep learning models.
  • AI has revolutionized pattern recognition, providing near instantaneous, high quality detection and classification of objects in complex, dynamic scenes.
  • DL can improve effectiveness of decision making skills by improving both the speed and quality at which tasks are detected and classified.
  • Such sources of bias can include implicit and unobserved bias from traits and characteristics by which persons identify themselves as part of a group (perhaps even subconsciously or unknowingly), leading to implicit biased group behavior. Since causes are unobserved for such implicit bias related to common group traits and biased group membership, modeling with explainable bias has yet to be developed.
  • BN Bayesian Network
  • DBM Deep-Bayesian Models
  • a deep Bayesian model learns prediction distributions and identifies group bias clusters from modeled prediction data and associated personal characteristics data. Key features from personal characteristics data related to group bias clusters are correlated in a fully connected dependency graph. A causal graph is constructed based on the dependency graph to identify causal relations of the key features to the group bias clusters.
  • Perturbation of individual key features having causal relations reveals sensitivity of features for more robust correlation of bias or preference to the prediction data according to particular features of personal traits, providing explainability in the deep learning model for latent bias.
  • a resultant model may enhance explainability in the following ways: (1) provide the details about why the DBM predicted a certain response for an individual; (2) directly provide which data descriptors (e.g., personal characteristic features) are responsible for the bias; (3) directly provide a rationale on how the data descriptors are related and which ones can produce the greatest response changes.
  • data descriptors e.g., personal characteristic features
  • FIG. 1 shows an example of a computer vision system with improved retrieval of best matched 3D models for target objects in accordance with embodiments of this disclosure.
  • FIG. 2A illustrates an example of a deep Bayesian model (DBM) in accordance with embodiments of this disclosure.
  • DBM deep Bayesian model
  • FIG. 2B illustrates a modified version of the DBM shown in FIG. 2B for estimating group bias clusters in accordance with embodiments of the disclosure.
  • FIG. 3 shows an example of flowchart for a process that models explainable latent bias extracted from decision modeling according to embodiments of this disclosure.
  • FIG. 4 illustrates an example of a computing environment within which embodiments of the disclosure may be implemented.
  • Methods and systems disclosed address the problem of understanding human bias within decisions, which has various applications in industry, such as learning the contribution of bias on user preference when constructing design software that can anticipate or estimate user preference based on past data with the user.
  • Other examples include software for forecasting, and root cause inferences, which can be affected by human bias developed from repeated past experiences.
  • Artificial intelligence observations of decision data is examined for patterns to develop group clusters suggesting group bias, from which deeper examination and perturbation can reveal explanation for which key features define the group and which factors would force a member out of the group. From this understanding, correlation and causality can be extracted to detect the presence of bias or preference for the observed decisions.
  • FIG. 1 shows an example of a system for a data driven model to infer group bias for an improved explainable model construction of human decision making in accordance with embodiments of this disclosure.
  • a system includes a computing device 110, which includes a processor 115 and memory 111 (e.g., a non-transitory computer readable media) on which is stored various computer applications, modules or executable programs.
  • modules include a preprocessing module 112, local deep Bayesian model (DBM) module 114, topic module
  • Local DBM module 114 is a client module used to interface with a cloud-based or web- based DBM I50 for determining group bias clusters from biased human decision data and event data 120, and personal characteristics/demographics data 125 (i.e., data descriptors for data 120).
  • Network 130 such as a local area network (LAN), wide area network (WAN), or an internet based network, connects computing device 110, DBM 150 and data repositories 120, 125.
  • LAN local area network
  • WAN wide area network
  • internet based network connects computing device 110, DBM 150 and data repositories 120, 125.
  • FIG. 2A illustrates an example of a DBM in accordance with embodiments of this disclosure.
  • a Bayesian network (BN) is useful for modeling prediction and has characteristics defined by distributions.
  • a deep Bayesian model (DBM) is applied, which implements deep learning (DL) networks to parameterize the distributions of the Bayesian network and to predict the parameters.
  • DBM 201 includes a DL network 210 and a BN 212 in which some
  • an unobserved variable p represents the true probability of some event x.
  • a human expert produces a time series of
  • RNN Recurrent Neural Network
  • each f i and n i is a random variable, and their relationship with p is a probability distribution, parameterized by the RNN 210.
  • a predictive model 212 can additionally include multiple forecasters, complex forecaster models, as well as any auxiliary data
  • Bayesian networks are interpretable by design. Visualizing the DL algorithms that estimate the functional relationships is very challenging.
  • the DL components can be hidden while only exposing the BN to the user.
  • the DBM decision models can be executed to yield useful action recommendations.
  • the DBM can compute a posterior p(x ⁇ data, model), which is the probability of a target variable x given the decision-making model as well as any available data.
  • Maximum- ⁇ -posteriori value of x is the optimal decision according to the model and data.
  • the basis of each suggestion is traceable through the network. Measures such as mutual information or average causal effect (ACE) quantify the strength of connections in a DBM
  • ACE average causal effect
  • One of the main benefits of using a Bayesian framework is the ability to evaluate models in a rigorous, unbiased way in terms of evidence, that is the likelihood of the data given model assumptions.
  • Computing model evidence involves, for all but the simplest models, solving a difficult non-analytical integration problem.
  • Traditional methods such as Markov Chain Monte Carlo or Nested Sampling are time-consuming and often require task-specific adjustments.
  • variational inference with DBM model evidence is a first-class object
  • approximate model evidence is directly optimized during training. Its approximation is readily available during training and inference. This enables the disclosed framework to support comparison and evaluation of competing models of decisionmaking.
  • the framework continuously re-evaluates the evidence of multiple competing models using streaming data.
  • FIG. 2B illustrates a modified version of DBM 201 for estimating group bias clusters in accordance with embodiments of the disclosure.
  • DBM 220 as a variation of the DBM shown in FIG. 2A, models observed time series event data x using temporal deep learning RNN 221. The response at time t is used to parameterize the distribution for the actual event probability p based on the eventual outcome of the surveyed predictions. The probability distribution p feeds into BN
  • RNN 221 models event data which occurred (e.g., questions and correct options), such as survey questions X ⁇ x 0 , ...,x t and participant response Y 6 y 0 , ..., y t .
  • KNN 221 models true probability p of events given historical data of predictions. The response y t at time t is used to parameterize the distribution for the true event probability p.
  • BN 222 (e.g., applying latent Dirichlet allocation or hierarchical Bayesian model) takes the input of the probabilities p of historical events from KNN model 221, latent bias estimates, and personal characteristics data PD to construct a prediction model f t , which models the distribution of observed decision data as predicted behavior F ⁇ f 0, ...,f t .
  • the BN 222 models an estimated bias distribution representing latent bias over time, modeled as a hidden node bias.
  • the initial distribution for the bias node is modeled by one or more prior parameters ⁇ .
  • the distributions pertaining to personal characteristics data PD, bias distribution bias and event probability p feed into the prediction model ft.
  • the relationships between these variables are probability distributions whose parameters are expressed by DL neural networks 211.
  • separate nodes are modeled for each category of personal characteristics data (e.g., competence, gender, technical experience).
  • the value of bias distributions which represent the characteristics of the bias clusters survey participants (reflecting age, competence, education, etc.) are estimated.
  • a curve fitting analysis is applied to solve for the bias distribution that best fits the prediction distribution f t to the actual prediction distribution p.
  • the final parameter values of the curve fitting function e.g., parameters of a latent Dirichlet analysis (LDA)
  • LDA latent Dirichlet analysis
  • the BN 222 incorporates an LDA algorithm as described above.
  • an LDA is useful for extracting topics from documents, where a document is a mixture of latent topics, each word is sampled from a distribution corresponding to one of the topics.
  • This functionality of an LDA is extended to an objective at hand in this disclosure, which is for sampling each decision from a distribution corresponding to one of the latent biases.
  • an LDA algorithm is applied to time series data 320 to group related tasks together, such that the DBM
  • FIG. 3 shows an example of flowchart for constructing an explainability model of human decision making that includes group bias inference.
  • a virtual (computer- based) decision model is sought for a particular task or topic in which decisions are critical to the task.
  • latent bias or preference is to be an included element.
  • the process for the disclosed framework involves modeling prediction or decision events for a given task domain based on collected data from numerous human predictions or decisions. From the prediction/decision data model, group bias clusters can be derived using a deep Bayesian model and correlated to common key features of personal characteristics and demographics data. Further processing includes causality graphs and perturbation for sensitivity, which yields an explainability model for latent bias or preference present in the prediction or decision data.
  • Time series human decision and event data 320 may be gathered from surveys (e.g., question/answer format) of multiple participants, the surveys related to prediction of future events.
  • Decision and event data 320 may capture forecast decisions over time fro participants to collect data related to future events useful for a prediction model. Participants may be asked questions related to predictions or forecast decisions for a target task or topic. For example, the questions may pertain to voting on options, or a yes/no option.
  • data set 320 may include data for as many as 1,000,000 to 3,000,000 predictions.
  • time series and event data 320 relates to observed behavior of participants in performing other types of tasks, other than forecasting.
  • the DL model may learn to predict binary decisions to perform or not perform a task in a given situation. In such cases, explainability of the DL model is sought with respect to latent bias affecting such decisions.
  • Personal characteristics/demographics data 325 are data descriptors for the time series event data 320, and may include a series of personal characteristics, such as gender, education level, and competence test scores for the surveyed individuals.
  • An objective when collecting the data may be to learn cultural influences (e.g., food, religion, region, language) which can identify common group traits of individuals, where normally bias is implicit and causes of decisions or predictions are unobserved. Examples of other traits leading to implicit biases discovered from prediction data can include one or more of the following: whether experience changes the voting behavior, whether age or gender influences the forecast decisions for a given topic, whether training changes to response to questions.
  • Personal characteristics/demographics data 325 may characterize competence and may be used for identifying bias traits.
  • detailed psychological and cognitive evaluation e.g., approximately 20 measures
  • decision-makers may include Raven's progressive matrix, cognitive reflection tests, berlin numeracy, Shipley abstraction and vocabulary test scores, political and financial knowledge, numeracy, working memory and similar test scores, demographic data (e.g., gender, age, education levels), selfevaluation (e.g., conscientiousness, openness to experience, extraversion, grit, social value orientation, cultural worldview, need for closure).
  • Data preprocessing 312 is performed on time series data 320 and personal characteristics/demographics data 325, and may include: (1) data cleaning of errors, inconsistencies and missing data; (2) data integration to integrate data from multiple files and for mapping data using relationships across files; (3) feature extraction for reduction of dimensionality, such as deep feature mapping (e.g., Word2vec) and feature reduction (e.g., PC A, tSNE); and (4) data transformation and temporal data visualization, such as normalization.
  • deep feature mapping e.g., Word2vec
  • feature reduction e.g., PC A, tSNE
  • Topic grouping module 316 performs exploratory topics data analysis, which generates results indicating event topic groups 321 for the survey questions x and can identify similar questions for explaining the effect of the tasks on the group bias clusters. As with cultural models, it is assumed that behaviors (e.g., decisions) of a group bias cluster would be dictated by the scenario under consideration, i.e., topic associated with the task in the dataset context The topic grouping module 316 groups related questions and event tasks together using an LDA analysis.
  • DBM module 314 receives data from task based model 313 and personal characteristics/demographics data 325, determines a prediction probability p from event data and determines estimated group bias clusters 335, using the process as described above in FIG. 2B, where event data x corresponds to time series data 320, and PD corresponds to personal characteristics/demographics data 325.
  • a cluster identifier function of DBM module 314 applies a parametric curve fitting analysis (e.g., latent Dirichlet analysis) to identify which participant belongs to which group bias cluster, and determines sets of group bias clusters from the input data. From the group clustering and associated data descriptors (personal characteristics/demographics), a key feature extractor of DBM module 314 identifies key features
  • DBM models are not classification models but are inspired from topic models
  • LDA latent Dirichlet analysis
  • evaluation criteria such as accuracy, precision-recall, and area under the curve are not applicable.
  • the evaluation is performed by first determining the topics of each document using LDA and then evaluating the appropriateness of the topics obtained.
  • a similar approach is performed, where the group bias clusters with shared personal-characteristics features indicate the key features which explain the groupings.
  • a cross-validation and a "cosine similarity metric" on the grouping is performed to obtain a numerical score.
  • Group bias models are determined based on each part using DBM 314. For each model, instance- wise feature selection is conducted for each user by the personal characteristic data. The common selected features under each group is determined for each model using cosine similarity.
  • DBM 314 determines if the same group bias cluster discovered by different data shares the similar common features.
  • the group matching may be determined by mapping a group with a group having highest Matthews correlation coefficient [0026]
  • Correlation module 317 takes each of the identified group bias clusters 335 and estimates the correlation between identified key features 336 through the use of a dependency network analysis, resulting in a fully-connected dependency graph with connections.
  • the dependency analysis network utilizes singular value decomposition to compute the partial correlations between features of the dependency network (e.g., by performing partial correlations between columns of the dataset, or between network nodes).
  • the computation of the dependencies is based on finding areas of the dependency network with highest “node activities”, defined by influence of a node with respect to other network nodes. These node activities represent the average influence of a node j on the pairwise correlations C(i,k) for all nodes i, k ⁇ N.
  • the correlation influence is derived by a difference between correlations C(i,k) and
  • PC as expressed by the following relationship: where i,j, k represent node numbers in the network.
  • a total influence D(ij) represents total influence of node j to node i, defined as average influence of node j on the correlations C(i,k), over all nodes k expressed as follows:
  • Node activity of node j is then computed as the sum value for D(ij):
  • a fixed number of top features e.g., top 10, 20, or SO
  • top 10, 20, or SO top 10, 20, or SO
  • Causality module 318 uses a subset of features from the results of the correlation module 317 to derive for each of the group bias clusters a causal graph 322 from a dependency graph by pruning non-causal relationships of the dependency graph.
  • the causal graph 322 provides the causal relationship between the participant characteristics/data descriptors (i.e., the dependency graph features) in the dataset for each group bias cluster and for all group bias clusters combined.
  • the causality analysis uses a greedy equivalence search (GES) algorithm to obtain the causal relations and to construct causal graphs.
  • GES is a score-based algorithm that greedily maximizes a score function (typically the Bayesian Information Criterion (BIC) score) in the space of essential (i.e., observational) graphs in three phases starting from the empty graph: a forward phase, a backward phase and a turning phase.
  • BIC Bayesian Information Criterion
  • GES algorithm moves through the space of essential graphs in steps that correspond to the addition of a single edge in the space of directed acyclic graphs (DAGs), the phase is aborted as soon as the score cannot be augmented any more.
  • DAGs directed acyclic graphs
  • the algorithm performs moves that correspond to the removal of a single edge in the space of DAGs until the score cannot be augmented anymore.
  • the algorithm performs moves that correspond to the reversal of a single arrow in the space of DAGs until the score cannot be augmented any more.
  • GES algorithm cycles through these three phases until no augmentation of the score is possible anymore.
  • GES algorithm maximizes a score function over graph space. Since the graph space is too large, a "greedy" method is applied. The rationale behind the use of GES scores for causality is as follows.
  • causal sufficiency refers to the absence of hidden (or latent) variables
  • Causal faithfulness is defined as follows: If X_A and X_B are conditionally independent given X_S, then A and B are d- separated by S in the causal DAG. However, empirically, if these assumptions do not hold, the performance of GES algorithm is still acceptable when number of nodes is not very large.
  • the causal relation may be pre-specified based on expert knowledge prior to obtaining the data-driven causal network using the GES algorithm.
  • the causality module 318 is configured to additionally perform counterfactual analysis to determine the effect of enforcing a particular edge on the causal graph 322.
  • the edge enforcement may be user-specified using a graphical user interface, on which GUI responsive changes to the network may be observed, based on observed data using the GES algorithm.
  • Perturbation module 319 refines the results of the causal module 318 so that bias explainability 375 can be inferred. While causal graph 322 gives relationships between nodes, it does not provide information about how much change to each node (node sensitivity) is enough to alter a survey response and/or group bias cluster membership of a participant. To estimate the changes, perturbation module 319 selects individual features from causal graph 322 (i.e., subset of features determined to be causal), perturbs the selected features in the DBM 314, and evaluates the response to group memberships. If the perturbation of a specific feature X results in a change in the question response for the majority of the group members, then the feature X becomes a likely explanation for the behavior of the group bias cluster for that specific topic. Bias explainability
  • 375 indicates the one or more personal characteristic features as being the highest influencers are most likely to be the cause of group bias in the decision and event data 310. For example, a sensitivity score may be assigned to each perturbed feature based on the number of group members that changed group affiliation (e.g., by changing answer to prediction survey question).
  • a sensitivity score may be assigned to each perturbed feature based on the number of group members that changed group affiliation (e.g., by changing answer to prediction survey question).
  • behavior of a group bias cluster would be dictated by the scenario, i.e., topic associated with the task in the dataset context, under consideration.
  • the perturbation of individual features derived from the causal graph can indicate a change in an individual’s perspective or preference to a certain task belonging to a given topic.
  • this preference change also contributes to the inferred bias explanation 375, indicating another factor of latent bias.
  • perturbation module To detect this topic based preference, perturbation module
  • 319 includes the event topic groups 321 in the explainability inference 375. If it is observed that the change in a certain feature results in a persistent change to an individual's perspective or preference across a majority of the tasks belonging to a certain topic, the observation identifies a relationship between personal characteristic, the topic associated with the event under consideration, and the bias with the model, providing a probabilistic estimate for the confidence associated with these estimates.
  • FIG. 4 illustrates an example of a computing environment within which embodiments of the present disclosure may be implemented.
  • a computing environment 400 includes a computer system 410 that may include a communication mechanism such as a system bus 421 or other communication mechanism for communicating information within the computer system 410.
  • the computer system 410 further includes one or more processors 420 coupled with the system bus
  • computing environment 400 corresponds to the disclosed system that infers bias from human decision data, in which the computer system
  • the processors 420 may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art More generally, a processor as described herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processor may also comprise memory storing machine-readable instructions executable for performing tasks. A processor acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and/or by routing the information to an output device. A processor may use or comprise the capabilities of a computer, controller or microprocessor, for example, and be conditioned using executable instructions to perform special purpose functions not performed by a general purpose computer. A processor may include any type of suitable processing unit including, but not limited to, a central processing unit, a microprocessor, a Reduced Instruction
  • RISC Complex Instruction Set Computer
  • ASIC Application Specific Integrated Circuit
  • the processor(s) 420 may have any suitable microarchitecture design that includes any number of constituent components such as, for example, registers, multiplexers, arithmetic logic units, cache controllers for controlling read/write operations to cache memory, branch predictors, or the like.
  • the microarchitecture design of the processor may be capable of supporting any of a variety of instruction sets.
  • a processor may be coupled (electrically and/or as comprising executable components) with any other processor enabling interaction and/or communication there-between.
  • a user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof.
  • a user interface comprises one or more display images enabling user interaction with a processor or other device.
  • the system bus 421 may include at least one of a system bus, a memory bus, an address bus, or a message bus, and may permit exchange of information (e.g., data (including computer- executable code), signaling, etc.) between various components of the computer system 410.
  • the system bus 421 may include, without limitation, a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and so forth.
  • the computer system 410 may also include a system memory 430 coupled to the system bus 421 for storing information and instructions to be executed by processors 420.
  • the system memory 430 may include computer readable storage media in the form of volatile and/or nonvolatile memory, such as read only memory (ROM) 431 and/or random access memory (RAM) 432.
  • the RAM 432 may include other dynamic storage device(s) (e.g., dynamic RAM static RAM and synchronous DRAM).
  • the ROM 431 may include other static storage device(s) (e.g., programmable ROM erasable PROM and electrically erasable PROM).
  • system memory 430 may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors 420.
  • a basic input/output system 433 (BIOS) containing the basic routines that help to transfer information between elements within computer system 410, such as during start-up, may be stored in the ROM 431.
  • RAM 432 may contain data and/or program modules that are immediately accessible to and/or presently being operated on by the processors 420.
  • System memory 430 may additionally include, for example, operating system 434, application modules 435, and other program modules 436.
  • Application modules 435 may include aforementioned modules described for FIG. 1 and may also include a user portal for development of the application program, allowing input parameters to be entered and modified as necessary.
  • the operating system 434 may be loaded into the memory 430 and may provide an interface between other application software executing on the computer system 410 and hardware resources of the computer system 410. More specifically, the operating system 434 may include a set of computer-executable instructions for managing hardware resources of the computer system
  • the operating system 434 may control execution of one or more of the program modules depicted as being stored in the data storage 440.
  • the operating system 434 may include any operating system now known or which may be developed in the future including, but not limited to, any server operating system, any mainframe operating system, or any other proprietary or non-proprietary operating system.
  • the computer system 410 may also include a disk/media controller 443 coupled to the system bus 421 to control one or more storage devices for storing information and instructions, such as a magnetic hard disk 441 and/or a removable media drive 442 (e.g., floppy disk drive, compact disc drive, tape drive, flash drive, and/or solid state drive).
  • Storage devices 440 may be added to the computer system 410 using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or
  • Storage devices 441, 442 may be external to the computer system 410.
  • the computer system 410 may include a user input interface 460 for graphical user interface (GUI) 461, which may comprise one or more input devices, such as a keyboard, touchscreen, tablet and/or a pointing device, for interacting with a computer user and providing information to the processors 420.
  • GUI graphical user interface
  • the computer system 410 may perform a portion or all of the processing steps of embodiments of the invention in response to the processors 420 executing one or more sequences of one or more instructions contained in a memory, such as the system memory 430. Such instructions may be read into the system memory 430 from another computer readable medium of storage 440, such as the magnetic hard disk 441 or the removable media drive 442.
  • the magnetic hard disk 441 and/or removable media drive 442 may contain one or more data stores and data files used by embodiments of the present disclosure.
  • the data store 440 may include, but are not limited to, databases (e.g., relational, object-oriented, etc.), file systems, flat files, distributed data stores in which data is stored on more than one node of a computer network, peer-to-peer network data stores, or the like. Data store contents and data files may be encrypted to improve security.
  • databases e.g., relational, object-oriented, etc.
  • file systems e.g., flat files
  • peer-to-peer network data stores e.g., peer-to-peer network data stores, or the like.
  • Data store contents and data files may be encrypted to improve security.
  • the processors 420 may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory 430.
  • hard-wired circuitry may be used in place of or in combination with software instructions.
  • embodiments are not limited to any specific combination of hardware circuitry and software.
  • the computer system 410 may include at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein.
  • the term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processors 420 for execution.
  • a computer readable medium may take many forms including, but not limited to, non-transitory, non-volatile media, volatile media, and transmission media.
  • Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks, such as magnetic hard disk 441 or removable media drive 442.
  • Non-limiting examples of volatile media include dynamic memory, such as system memory 430.
  • Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up the system bus 421.
  • Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
  • Computer readable medium instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
  • the computing environment 400 may further include the computer system 410 operating in a networked environment using logical connections to one or more remote computers, such as remote computing device 473.
  • the network interface 470 may enable communication, for example, with other remote devices 473 or systems and/or the storage devices 441, 442 via the network 471.
  • Remote computing device 473 may be a personal computer (laptop or desktop), a mobile device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system 410.
  • computer system 410 may include modem 472 for establishing communications over a network 471, such as the Internet.
  • Modem 472 may be connected to system bus 421 via user network interface 470, or via another appropriate mechanism.
  • Network 471 may be any network or system generally known in the art, including the
  • the network 471 may be wired, wireless or a combination thereof. Wired connections may be implemented using Ethernet,
  • USB Universal Serial Bus
  • RJ-6 or any other wired connection generally known in the art
  • Wireless connections may be implemented using Wi-Fi, WiMAX, and Bluetooth, infrared, cellular networks, satellite or any other wireless connection methodology generally known in the art
  • networks may work alone or in communication with each other to facilitate communication in the network 471.
  • API(s) Programming Interface(s)
  • any other suitable computer-executable code hosted locally on the computer system 410, the remote device 473, and/or hosted on other computing device(s) accessible via one or more of the network(s) 471 may be provided to support functionality provided by the program modules, applications, or computer-executable code depicted in FIG. 4 and/or additional or alternate functionality.
  • functionality may be modularized differently such that processing described as being supported collectively by the collection of program modules depicted in FIG. 4 may be performed by a fewer or greater number of modules, or functionality described as being supported by any particular module may be supported, at least in part, by another module.
  • program modules that support the functionality described herein may form part of one or more applications executable across any number of systems or devices in accordance with any suitable computing model such as, for example, a client-server model, a peer-to-peer model, and so forth.
  • any of the functionality described as being supported by any of the program modules depicted in FIG. 4 may be implemented, at least partially, in hardware and/or firmware across any number of devices.
  • the computer system 410 may include alternate and/or additional hardware, software, or firmware components beyond those described or depicted without departing from the scope of the disclosure. More particularly, it should be appreciated that software, firmware, or hardware components depicted as forming part of the computer system 410 are merely illustrative and that some components may not be present or additional components may be provided in various embodiments. While various illustrative program modules have been depicted and described as software modules stored in system memory 430, it should be appreciated that functionality described as being supported by the program modules may be enabled by any combination of hardware, software, and/or firmware. It should further be appreciated that each of the above-mentioned modules may, in various embodiments, represent a logical partitioning of supported functionality.
  • This logical partitioning is depicted for ease of explanation of the functionality and may not be representative of the structure of software, hardware, and/or firmware for implementing the functionality. Accordingly, it should be appreciated that functionality described as being provided by a particular module may, in various embodiments, be provided at least in part by one or more other modules. Further, one or more depicted modules may not be present in certain embodiments, while in other embodiments, additional modules not depicted may be present and may support at least a portion of the described functionality and/or additional functionality. Moreover, while certain modules may be depicted and described as sub-modules of another module, in certain embodiments, such modules may be provided as independent modules or as sub-modules of other modules.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block may occur out of the order noted in the Figures.
  • two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

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Abstract

L'invention concerne un système et un procédé de détection de polarisation latente par modélisation d'intelligence artificielle de prise de décision humaine à l'aide de données de prédiction de série chronologique et de données d'événements de participants à l'étude conjointement avec des données de caractéristiques personnelles pour les participants. Un modèle bayésien profond résout une distribution de polarisation qui s'adapte à une distribution de prédiction modélisée de données d'événements de série chronologique et de données de caractéristiques personnelles à une distribution de probabilité de prédiction dérivée par un réseau de neurones récurrents. Des ensembles de groupements de polarisation de groupe sont évalués pour des caractéristiques clés de caractéristiques personnelles associées. Des graphiques de causalité sont définis à partir de graphiques de dépendance des caractéristiques clés. L'explicabilité de polarisation est inférée par perturbation dans le modèle bayésien profond d'un sous-ensemble de caractéristiques depuis le graphique de causalité, déterminant quelles relations causales sont les plus sensibles pour modifier l'appartenance à un groupe de participants.
EP20771681.2A 2019-12-30 2020-08-28 Détection de polarisation et explication de modèles d'apprentissage profond Pending EP4066166A1 (fr)

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US11941500B2 (en) 2021-12-10 2024-03-26 Agile Systems, LLC System for engagement of human agents for decision-making in a dynamically changing environment
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