Measurements of trust in AI environment
- Ethical AI
- Multicloud edge orchestration beyond 5G connectivity
- 360 SOC
- 5G Chargers
- 5G cloud & AI convergence of BOSS
- 5G digital marketplace - Phase II
- 5G Enhanced manufacturing
- 5G Flyers - Phase II
- 5G next-gen performance with cloud gaming
- 5G slicing as a service for B2B2X over convergent cloud central offices
- 5G: Accelerating smart aviation (Phase IV)
- Access! is access!! is access!!! The road to ODA converged fixed & wireless access
- AI empowers 5G intelligent operations - Phase II
- AI Ethics
- AI factory marketplace
- AI-based 5G lifecycle resource management
- An intelligent low-code platform that empowers the digital transformation of traditional enterprises
- Automated Operational Problem Solver using AI and AR
- Autonomous CEI for zero-touch 5G network- Phase II
- Autonomous customer experience index for zero-touch 5G network
- Autonomous networks empowered smart energy saving
- Autonomous networks hyper loops for disruptive digital services - Phase III
- Autonomous networks hyperloops - Enabling self-X digital services for smart-X verticals Phase II
- B2B service integration
- BCE based roaming settlement on blockchain
- Blockchain-based equity exchange alliance
- Catalyst Demonstration project
- Channels and markets
- Collaborative partner on-boarding - Phase II
- Competitive advantage with AI-driven offer design and lifecycle management
- Comprehensive CEx model for dynamic B2B(2X) business
- Connected digital twins for IoT
- Cross-industry marketplace for CSP collaboration Phase 3
- Customer avatar: The key to unlock total experience of telco operation in Metaverse
- Data and value driven operation through ProcM process mining
- Data reconciliation between operators using Blockchain
- DBM Phase 4: Smart Mining
- Digital Business Marketplace - Phase 4
- Digital network twin for data-driven 5G services
- Digital services marketplace - Phase III
- Digital transformation of Hawe Telekom
- Digital twin network application in 5G network operation
- DONEG-AI: Best practices for operationalizing AI
- Dynamic edge enabled applications
- Ecosoft eHealth
- Ecosystem platform for smart city campus networks
- Edge AI for smart agriculture
- Enhanced e-channel marketing
- Force 10 - The vertical industries enabler for telecommunications
- Green AI for autonomous networks
- Heidi's Test Project
- How to serve a global network with multiple CSPs efficiently
- Intelligent operation of 5G cloud-network convergence services for vertical industries
- Intelligent operations of leased line services
- Intent-driven autonomous networks
- Intent-driven autonomous networks for smart mobility
- Lean and agile management of the marketplace, digital operation assessment, and intelligent decision-making
- Leveraging AI/ML to drive CX business outcomes proactively
- Low code agile marketing operations platform
- Make banks smarter with autonomous network
- Managing and selling non-CSP products
- Measurement of trust in AI environment - Phase II
- Measurements of trust in AI environment
- MLOps to improve lowcode based AI
- Multi-Operator/Multi-Vendor Resource Pooling and RAN Slicing
- Network O&M transformation
- One-stop 5G business to business (B2B) services
- Open digital twin framework for smart city ecosystem
- Operations-as-a-service
- Partner settlement capability in 5G ecosystem
- Privacy computing for green grid
- Scale blockchain-based data sharing
- Service Mesh for cloud-native business applications
- Settlements on co-branded gift cards
- Silent global roamers
- Simplifying the network and computing integration in 5G networks
- Smart building management-in-a-box
- Smart decision – An AI-empowered decision making intelligence to drive autonomous business operations
- Smart home business proactive and predictive maintenance
- Smart IDC - Intelligent energy saving for data centers
- Smart O&M for private data centers
- Standard YANG equipment model
- Standardized value-evaluation model for digital assets
- Supercharge 5G monetization with a B2B2X marketplace
- Supercharge 5G monetization with a B2B2X marketplace - Phase II
- Sustainable growth for enterprises with 5G and MEC operations
- Telco data space
- Telco data-space - Phase II
- TM Forum Open APIs in an event-driven architecture
- Translating customer needs into service and network intent
- Trusted data sharing for financial industry
- Unified MEF/TMF compatible API interoperability
- When AI becomes an expert in improving user experience
- Wireless network autonomy management based on knowledge graph
- Zero Touch Operations (ZTO)
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Updates
Ramya Raj
29 Sep 2021 06:00
0 Comments
Hello,
please check the resources section to get details of this catalyst. If you would like to see the demo and know more about it, our team is available and you could simply click on "Book a meeting" button to fix up some time.
Ramya Raj
09 Sep 2021 07:46
0 Comments
For AI to gain mass adoption, it needs to be trusted by governments, enterprises, and users directly or indirectly impacted by such applications. A trusted AI framework is then imperative. A failed AI program can not only lead to customer churn, reduced profitability and litigation but can also result in loss of reputation and regulatory scrutiny.
So, how do we make AI systems more trustworthy?
Subex, ADL, Bolgaiten, Brytlyst & AWS have joined hands with our champions like Axiata, Ncell Axiata & Dialog to address the trust issues in AI systems by building a comprehensive framework to measure trust. We have picked up 3 use cases to measure trust:
1. Churn Prediction
2. Maintaining Model Trust in Real-Time Analytics
3. Credit Rating
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Collaboration
Description:
C21.0.213
Status: Completed
Completed: This project has completed this phase and is no longer active.
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Completed: This project has completed this phase and is no longer active.
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URN
C21.0.213
Themes
Customer Experience & Trust
Topics
AI/Machine Learning
, Analytics
, IT Transformation
, Open Digital Architecture
This Catalyst project aims to build a system of trust within the Artificial Intelligence (AI) environment that adheres to the ethics and governance policies of organizations. The system will be based on a comprehensive framework that uses qualitative and quantitative measures to assess trust factors throughout the AI lifecycle (i.e., explainability, fairness, privacy, reliability and security).
Establishing a system of trust will ensure any AI system is safe, technically robust, transparent, accountable, non-discriminative and able to mitigate bias. A trusted ecosystem that includes internal and external model development will be able to expose potential risks as early as possible.
Communications service providers (CSPs) have started to experience some benefits from early AI implementations, such as reducing costs and introducing revenue-generation schemes. But there remains a large trust gap between AI and its beneficiaries that could deter further adoption and limit its potential. CSPs need certainty that AI models and algorithms will not expose their brands to negative or legal risks by violating their own corporate ethics and governance policies.
The communications industry must take adequate measures to mitigate AI technology’s negative impact.
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