APAC CIO Outlook
  • Home
  • CXO Insights
  • CIO Views
  • News
  • Conferences
  • Newsletter
  • Whitepapers
  • About us
Apac
  • Admired Tech

    Agile

    AI Healthcare

    Artificial Intelligence

    Augmented Reality

    Aviation

    Big Data

    Blockchain

    Cloud

    Cryptocurrency

    Cyber Security

    Digital Transformation

    Drone

    HPC

    Infrared

    Internet of Things

    Networking

    PropTech

    Remote Work

    Scheduling Software

    Simulation

    Startup

    Storage

    Wireless

  • Banking

    E-Commerce

    Education

    FinTech

    Food and Beverages

    Healthcare

    Insurance

    Legal

    Manufacturing

    Pharma and Life Science

    Retail

    Travel and Hospitality

  • Atlassian

    CISCO

    Microsoft

    Oracle

    Salesforce

    SAP

    ServiceNow

  • Business Intelligence

    CEM

    Cloud-based Planning

    Cognitive

    Compliance

    Contact Center

    Contact Tracing

    Contactless Payments

    Content Management System

    Corporate Finance

    CRM

    Custom Software Development

    Data Center

    Enterprise Architecture

    Enterprise Communications

    Enterprise Contract Management

    ERP

    Field Service

    HR Technology

    IT Service Management

    Managed Services

    Procurement

    Product Management

    RegTech

    Revenue Management

    Sales Tech

Menu
    • Cognitive
    • Augmented Reality
    • Agile
    • Cyber Security
    • Digital Transformation
    • Atlassian
    • E-Commerce
    • Managed Services
    • RegTech
    • CISCO
    • Blockchain
    • IoT
    • MORE
    #

    Apac CIO Outlook Weekly Brief

    ×

    Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Apac CIO Outlook

    Subscribe

    loading

    THANK YOU FOR SUBSCRIBING

    • Home
    • Cognitive
    Editor's Pick (1 - 4 of 8)
    left
    Seamless Integration into Networking Industry

    Robert Lewis, CIO, Assurant

    Enhancing Customers' Experience through Technology

    Marc A. Hamer, VP & CIO, Babcock & Wilcox Enterprises, Inc.

    Strategies to Unleash the full Potential of your Intelligent Automation (IA) Initiative

    Adrian Iaiza, Formerly Head of Process Automation and Improvement, TAL Australia

    Building A Business Case for RPA

    Pavel Gimelberg, Director, Head of Intelligent Automation Practice APAC, EPAM Systems

    The 'Cognitive Enterprise' in a 4th Industrial Revolution World

    Rocky Scopelliti - Futurologist & Author

    Emergence of Cognitive Enterprises

    Aaron Tan, Head Of Data & Analytics, SGX

    Cognitive Technology: Keys to Unlocking the Future of Business

    Steve Ng, Lead, Digital Platform Operations, Digital Group, Mediacorp PTE Ltd

    Smarter Information Supply Chains

    Pete Crawford, Head of Data, Analytics & Automation, Sensis

    right

    Responsible AI: The Human-Machine Symbiosis

    By Sal Cucchiara, CIO & Head Of Wealth Management Technology, Morgan Stanley

    Tweet
    content-image

    Sal Cucchiara, CIO & Head Of Wealth Management Technology, Morgan Stanley

    Over 20 years ago, a supercomputer beat world chess champion Garry Kasparov at chess. That moment helped plant the seeds for the era of big data in which we live today, and it marked a critical cultural turning point that suggested a machine could outsmart a human after all.

    While Kasparov initially expressed cynicism regarding the computer’s methods and its intelligence, he has more recently changed his tune, crediting the power of artificial intelligence (AI) and advocating for a symbiotic relationship between humans and machines. “At the end of the day,” he argued, “it is for us to even explain when something is successful. It is still for us to define success and machines to perform their duty” – underscoring the significance of our human role in defining and creating the knowledge base, the logic, and the authority that we empower our AI systems to wield.

    What does this mean for those of us who create AI systems, in the era of big data, and in an era where consumers are (rightfully) expecting and demanding that we leverage that data responsibly and accurately? To ensure both consumer trust and high-quality products and services, AI systems need to maintain a high degree of data integrity, enable end-users effectively, and employ responsible use safeguards.

    Data Integrity

    Data integrity sits at the core of AI systems and is the foundation of client trust. Consumers hold higher expectations of accuracy for machines than for humans, however all machine intelligence is derived from human inputs. Our AI systems have the capacity to deliver this accuracy if we have an exacting grip over how our data is identified, collected, maintained, and integrated into our systems.

    The first step to achieving this is through effective data curation, which involves identifying the most authoritative, trustworthy source for each piece of data, and then structuring the data in such a way that makes it easily accessible and eliminates ambiguity. We then need to implement robust knowledge management practices to continuously ensure that information remains up-to-date. We must account, for example, for any and all events that impact our data, from world events, to regulatory changes, to individual client events. Finally, AI systems must implement built-in feedback loops, to give technologists visibility into how the system interacts with end-users, and to allow end-users to communicate the accuracy of the answers that AI systems provide.

    Beyond the answers themselves, end-users also tend to demand the reasoning behind these answers.

    While a common myth conflates Machine Learning (MLl) with AI itself, ml is merely the tool that renders systems Artificially Intelligent

    For example, if a client’s request to execute a trade is denied, he or she will generally want to know why. To engender optimal trust, AI systems must thus also enable model explainability, to provide evidence for its responses and actions. If a system detects a transaction to be fraudulent, it should be able to provide evidence for its detection. If a ChatBot provides an answer to a question about stock prices, it should also be able to link to its source. When an AI system provides evidence in support of its response or actions, users can trust it more completely, and can also indicate whether the evidence itself makes sense in the feedback.

    End User Enablement

    The goal of AI, at its core and across industries, should be to help users optimize the work that they do—whether by solving problems more efficiently or by adopting tactical work and enabling its users to think and operate more strategically.

    One way we’re using AI at Morgan Stanley is to support our financial advisors in managing their client relationships. On one level, we’re doing this by employing AI to automate time-consuming manual tasks. Automating these tasks makes our branch staff considerably more efficient, and allows our financial advisors to reinvest their time in building client relationships.

    We’re also using AI to help our financial advisors build these very relationships more strategically through our Next Best Action (NBA) platform. NBA optimizes financial advisors’ daily activities with prioritized recommendations that are instantly actionable, employing a task-ranking algorithm that enables financial advisors to dedicate their time to the most valuable tasks at any given moment. For example, NBA would notify a financial advisor of a client’s life event such as changing jobs and advise the financial advisor to reach out accordingly, and quickly.

    Recommendations are ranked in terms of predicted value, the likelihood of the financial advisor and client to act, clients’ optimal contact schedules and indicators of potential attrition. NBA’s integration with client relationship management applications enables financial advisors to execute recommendations with scale and ease: it takes just a few clicks to initiate bulk client engagement activities such as emailing cybersecurity recommendations or executing on an investment idea that many clients qualify for. The platform constantly learns by tracking which actions financial advisors enact versus ignore, and leverages this learning to constantly improve future suggestions.

    These are just a few examples from the wealth management side of our business.

    Responsible Use

    AI systems should be built with a certain level of self-awareness to ensure that they are only used for tasks they can competently handle and accurately deliver on. To facilitate this, we build algorithms into our systems that constantly score the system’s confidence in responding to requests. Confidence scoring helps prevent our AI systems from providing erroneous information to our clients. Complex or ambiguous questions receive lower confidence scores, which indicate when humans are better suited to handling the given task.

    Effective and responsible AI also necessitates a vigilant safeguarding of client data. To protect our clients, we maintain robust physical, electronic, and procedural safeguards that are designed to guard client information against misuse or unauthorized access.

    While a common myth conflates machine learning (ML) with AI itself, ML is merely the tool that renders systems artificially intelligent. Humans still need to teach the machine how to learn, and ultimately, a system is only ever as intelligent as the data that underpins it. Our role and our responsibility in creating successful AI systems thus remain central, ongoing and necessarily human.

    tag

    Financial

    Big Data

    Machine Learning

    Wealth Management

    Weekly Brief

    loading
    Top 10 Cognitive Technology Companies - 2020
    ON THE DECK

    Cognitive 2020

    Top Vendors

    Cognitive 2019

    Top Vendors

    Previous Next

    I agree We use cookies on this website to enhance your user experience. By clicking any link on this page you are giving your consent for us to set cookies. More info

    Read Also

    The Changing Landscape of Cyber Security

    The Changing Landscape of Cyber Security

    Scott Brandt, CIO & Director of IT, Texas Office of the Secretary of State
    Accelerating Petcare Innovation through CRM and Digital Vision

    Accelerating Petcare Innovation through CRM and Digital Vision

    Miao Song, Chief Information Officer, Mars Petcare
    How Cloud Systems are Impacting Business Environments

    How Cloud Systems are Impacting Business Environments

    Martin Stegner, CIO, NOVUM Hospitality
    Digital Tack

    Digital Tack

    Claus Nehmzow, Chief Innovation Officer, Eastern Pacific Shipping Pte
    Brokering the Cloud Services

    Brokering the Cloud Services

    Eric Boyette, Secretary & State CIO, Information Technology
    Defining a Cloud Strategy: A Higher Education Paradigm

    Defining a Cloud Strategy: A Higher Education Paradigm

    Russell M. Kaurloto, VP and CIO, Clemson University
    The 4Ps of Digital Transformation in Pharmaceutical Industry

    The 4Ps of Digital Transformation in Pharmaceutical Industry

    Debraj Dasgupta, Operating Officer, Head of Strategy and Go-To-Market Planning Division, Nippon Boehringer Ingelheim
    Technology’s Role in The Care and Quality of Life for The Aged

    Technology’s Role in The Care and Quality of Life for The Aged

    Jose A Perez, Chief Information Officer, Hammondcare
    Loading...

    Copyright © 2021 APAC CIOoutlook. All rights reserved. Registration on or use of this site constitutes acceptance of our Terms of Use and Privacy and Anti Spam Policy 

    |  Sitemap |  Subscribe

    follow on linkedinfollow on twitter follow on rss
    This content is copyright protected

    However, if you would like to share the information in this article, you may use the link below:

    https://cognitive.apacciooutlook.com/ciospeaks/responsible-ai-the-humanmachine-symbiosis-nwid-6314.html?utm_source=google&utm_campaign=apacciooutlook_topslider