Abstract: Ethics-based decision making is disclosed. Outputs of artificial intelligence processes are tagged or labeled with ethics scores. When decisions are made using the outputs, the ethics tag inform the decision engine regarding the ethics of the outputs. The decisions can, based on the ethics scores, proceed or be delayed until additional input is received. In one example, the selection of an asset may depend the output ethics score.

Abstract: One example method includes receiving an orchestration automation request for an asset, identifying an ethics rule that applies to the asset, comparing the ethics rule to asset values contained in an AI ethics datastore, based on the comparing, identifying a list of assets that conform to the ethics rule, and when the asset appears in the list of assets that conform to the ethics rule, automatically placing the asset at an entity of a computing infrastructure.

Abstract: One example method includes for each pillar in a group of AI ethics pillars, storing, in a datastore, context data concerning the AI ethics pillar, and the context data is determined using context rules. The method further includes storing, in the datastore, the context rules as minimum context requirements, and receiving, by the datastore, a request from a user to register an asset in the datastore. When user-supplied context information for the asset meets ethical requirements specified by the context rules, registering the asset in the datastore, and ensuring that an assessment mechanism is able to access, and assess, the context data for each AI ethics pillar.

Abstract: Techniques for leveraging data in a data marketplace environment are provided. For example, a method comprises the following steps. A representation of one or more data valuations for a given data set is obtained. The representation of the one or more data valuations for the given data set is converted into a value for presentation to a data marketplace environment. The data marketplace environment comprises one or more data marketplace protocols that each enable one or more data consumers to obtain data. The method brokers a transfer of the given data set through at least one of the data marketplace protocols to at least one data consumer. The method manages transfer of the given data set to the at least one data consumer and receipt of a result of the transfer from the at least one data consumer.

Abstract: A given policy file is obtained at a publishing node of a decentralized system of nodes, wherein the given policy file defines a policy that applies to at least a subset of nodes in the decentralized system of nodes. The given policy file is sent to a decentralized storage network for storage therein. Storage metadata is received from the decentralized storage network, wherein the storage metadata represents address information associated with storage of the given policy file in the decentralized storage network. The publishing node generates policy file retrieval metadata based on the storage metadata received from the decentralized storage system. The policy file retrieval metadata is sent to a blockchain network for storage therein. One or more querying nodes of the decentralized system of nodes access the blockchain network to obtain the policy file retrieval metadata in order to then retrieve the policy file from the decentralized storage network.

Abstract: One example method includes receiving data at a node of a data confidence fabric, annotating, at the node, the data with an annotation that includes data confidence information, receiving a ledger stream at a ledger, and the ledger stream includes the annotation, and a representation of the data, creating, in a data structure associated with the ledger, a view node that corresponds to the data, creating, in the data structure, a representation of the annotation, and connecting, in the data structure, the representation of the annotation to the view node with an annotation edge.

Abstract: In a multi-cloud computing environment comprising a plurality of cloud platforms with each cloud platform comprising one or more nodes, a method maintains a decentralized metadata database framework, wherein each node comprises a decentralized metadata database component operatively coupled to each other decentralized metadata database component of the framework and wherein each of at least two of the decentralized metadata database components stores a set of metadata records corresponding to protected data stored across the plurality of cloud platforms. Further, the method manages one or more access requests directed to the protected data through one or more of the decentralized metadata database components of the framework.

Abstract: Data management is disclosed that detects changes in production or potential changes in production to trigger business decisions that inform business spend. Sensor input meant to monitor manufacturing and event correlation data can serve as input to trigger business decisions. The decisions can account for cost structures to ensure automated investments in the interest of the business or other organization. The data management links an early understanding of production data and business decisions to increase the link between manufacturing investment and business spend and to decrease waste including economic waste.

Abstract: One example method includes assessing an environment using a data confidence fabric. Data from sources associated with an environment such as a remote working environment is ingested into the data confidence fabric and associated with confidence scores. The environments can be assessed or monitored to ensure that the conditions of the environment comply with a standard.

Abstract: Routing data in a data confidence fabric. Data ingested into a data confidence fabric is routed to maximize confidence scores and to minimize the amount of missing confidence information. Routing is based on a configuration file and on pathing map information that allows nodes capable of applying the trust insertions set forth in the configuration file to be identified.

Abstract: One example method includes checking an asset against an Inclusion List and/or an Exclusion List to determine if the asset is permitted to contribute data, generated by the asset, to an enterprise data confidence fabric, when the asset is present on the Inclusion List, or not present on the Exclusion List, designating the asset as a trusted asset and appending the data generated by the asset to a ledger of the enterprise data confidence fabric, updating a ledger content index to reflect the data that was appended to the ledger, and annotating the data generated by the asset with trust metadata.

Abstract: A trust broker is disclosed for a data confidence fabric. The trust broker evaluates the trustworthiness of data flowing through a network that includes a data confidence fabric. The trust broker evaluates a baseline confidence score and generates a workorder to improve the baseline confidence score in a measurable way. The trust broker may implement the workorder and ensure that the trust improves in the data confidence fabric.

Abstract: In a multi-cloud computing environment comprising a plurality of cloud platforms, wherein one cloud platform is a source of a model and a data set and further wherein the model is to be executed against the data set on one or more of the other cloud platforms, the method maintains a decentralized architecture comprising a file system and a message bus, wherein the file system comprises a plurality of decentralized file system nodes corresponding to the plurality of cloud platforms, and the message bus comprises a plurality of decentralized message bus nodes corresponding to the plurality of cloud platforms. Further, the method manages sharing of the model and the data set via at least a portion of the decentralized file system nodes and manages messaging related to execution of the model against the data set via at least a portion of the decentralized message bus nodes.

Abstract: One example method includes identifying emerging hybrid technologies. Datasets are data mined to identify transactions that are related to technology. Associations are generated based on the transactions and the associations are processed to identify emerging hybrid technologies from the transactions.

Abstract: Data valuation leveraging techniques in a sensor data environment are provided. For example, a method obtains, at a gateway, at least one value computed by at least one data valuation algorithm for at least one sensor data element generated by at least one sensor associated with a set of one or more sensors operatively coupled to the gateway. The method then leverages, by the gateway, the at least one value computed for the at least one sensor data element.

Abstract: Mapping applications or smart contracts to a data confidence fabric. A smart contract is deployed and executed in a data confidence fabric based on trust requirements of the smart contract. The trust requirements are mapped to the nodes of the data confidence fabric. A ledger is created on the identified nodes and the application is deployed to and run on the identified nodes.

Abstract: One example method includes identifying datasets known to contain data relating to different technologies, extracting the data from the data sources, performing preprocessing on the data, clustering the data after the data has been preprocessed, and the clustering comprises generating data clusters, and mapping each of the data clusters to a phase of a Wardley Map. The mapping may be used to make a prediction about which phase a particular technology would fall under in a future time period.

Abstract: One example method includes signal of risk access control. Access control is performed based on metadata that includes at least an intent of use, a requestor, and an end user. Access to combinations of data or data sets are based on context metadata that is evaluated at the time of the request instead of after the fact. The context metadata and other labels applied to the data or data sets can be used to orchestrate data access control operations.

Abstract: A physical event to be modeled is selected. A profile for the physical event is generated based on an event type of the physical event. Data is obtained from a plurality of data sources, wherein the obtained data comprises data relevant to the physical event that is collected by the plurality of data sources, and further wherein at least a portion of the obtained data comprises one or more of spatial and temporal references associated with the collection of the data. A digital representation of the physical event is generated based on at least a portion of the obtained data and the generated profile. The digital representation is utilized to analyze one or more other physical events associated with the modeled physical event.

Abstract: One example method includes receiving a transaction at a digital twin that incorporates all transactions that have occurred at a site from which the transaction was received, and wherein the digital twin was created based in part on a data confidence fabric ledger, entering the transaction in the data confidence fabric ledger at the digital twin, receiving another transaction at the digital twin, wherein the another transaction has caused a problem to occur, entering the another transaction in the data confidence fabric ledger, replaying any transactions that have occurred in a defined time window that includes the another transaction, based on the replaying, identifying a state of a system where the problem occurred, and a time when the problem occurred, and determining a resolution to the problem.