A Case for Artificial Intelligence in

Future Procurement

CHRIS ROBEY, U.S. CUSTOMS AND BORDER PROTECTION, U.S. DHS

The views expressed in this presentation are those of the author and do not reflect the policy or position of U.S. Customs and Border Protection, U.S. Department of Homeland Security, or the U.S. Government.

Agenda

State of the art

What is a business agent?

AI Business Agent Typology – Welcome to the zoo!

AI Business Agents in Negotiations – Who do you trust?

AI Business Agents in Audits – Are you ready?

Conclusion

Sources

Q&A

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State of the Art - Thresholds

Bots and algorithms using structured data?

Turing Test?

General intelligence AI using unstructured data?

Superhuman intelligence AI? 20 years out, per Bostrom

Opinion: Agnostic whether AI can achieve consciousness, will, or intent, as understood within human experience:

(h)owever, goal-directed and adaptive strategic behavior by decision support systems with domain expertise is very much within the current state of the art of cognitive computing.1

1 Sue Feldman and Hadley Reynolds, “Cognitive computing: A definition and some thoughts,” KW World, November/December 2014, ref. http://www.kmworld.com/Articles/News/News-Analysis/Cognitive-computing-A-definition-and-some-thoughts-99956.aspx

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What is a business agent? Agent

A software entity that acts autonomously, that is, makes its own decisions on behalf of the designer, typically in dynamic environments from which it learns and to which it adapts.

When applied to the development of new internet technologies, agents need also to show a social attitude.1

Which leads us to consider:

Multi-agent system (MAS)

A collection of autonomous agents that need to coordinate their activities in order to achieve their individual goals.

Coordination is achieved through negotiation or argumentation and, in most applications, requires that the agents learn to adapt to each other's strategies.2

1,2 Keith Frankish and William Ramsey, ed., The Cambridge Handbook of Artificial Intelligence, Cambridge University Press, 2014, Pages 335, 339

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AI Business Agent Typology

Current SOA

In operation

SCM and logistics/

transportation/

scheduling algorithms

Retail and B2B

transactions

Can negotiate using

structured data

Will take off with ISO

standards and NIST

policy (both TBD)

BA advisor

Emerging SOA

Aid to human operators in negotiation and maintain institutional knowledge

Natural-language, real time query-and-response access to enterprise-wide

Finance

Procurement

Operations

databases delivered securely as a cloud service

As aid through differing Internet of Things (IoT) standards/protocols/ platforms for SCM

AI-enabled BA’s

Future SOA

General-purpose AI in

autonomous BA’s

Unstructured data

Capable of negotiation

within multi-agent

environment

Legal status of contracts

formed – uncertain (?)

In labs – 5 years out (?)

Competition will drive

technical progress

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Bots & web crawlers

AI Business Agents in Negotiations Disruption to processes

Business capture

Execution/operations

Audit

Why?

Compelling economies

Competitive pressure

Scalability

Applicability to business processes

Unforeseen consequences

Evolving models of trust need for governance

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Models of trust in negotiations

1.0 Human/legal

Counterparty:

Has authority as

agent to represent

his/her principal

Will provide

accurate/truthful

responses to factual

queries, i.e., no

fraudulent intent

Will carry out the

bargain upon

agreement

2.0 E-commerce

Both consumer and

business-to-business

(B2B) protocols:

Availability

Visibility

Security

Transaction

execution

New questions?

Emergence of cloud-

based, business-to-

business (B2B)

ecommerce:

Do you trust the data

structures of your

transaction partners?

How will certification/

accreditation processes

evolve with the threat

environment?

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Models of trust in negotiations

E. Alonzo on social rationality among BA:

Sincerity: No agent will attempt to have another believe a proposition that it either knows or believes to be false or a proposition that it wants to be false

e.g., agents cannot commit themselves to execute actions that they are not able to perform.

Honesty: Agents have to act according to their beliefs.

Fair play: Agents must abide by the agreed deals.

Sociability: In case of indifference, agents must accept others' offers, and deals must always be individually rational.1

1 Eduardo Alonzo, “Actions and Agents,” from The Cambridge Handbook of Artificial Intelligence, Cambridge University Press, 2014, Page 240.

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3.0 AI-enabled business agents (in multi-agent systems)

Who do you trust? 9

BA (Irene) and human (Brian) in mock

negotiations for IT hardware

Academic experiment to explore how agent

behaviors affect success/failure of negotiations

with human subjects segmented by personality

type

From IEEE Intelligent Systems, March/April 2014, pps. 36 - 43; Figure 1 used with permission of IEEE.

Social Engineering Big Data Analytics

Descriptive

Predictive

Prescriptive

Intention analysis vs. sentiment analysis in user interaction with

websites1

High-volume retail websites will test new consumer-facing algorithms in

competition against each other, while using metrics of user behavior to

determine which prototype to scale up across the website

B2B traffic will not be exempt from the lessons learned.

Watch for “Moneyball”-style metrics on individual human negotiators on

the other side, i.e., characteristics and negotiating style, to be gathered

by AI BA’s for sharing with other entities in the enterprise1Jeff Bertolucci, “Big Data Tool Analyzes Intentions: Cool or Creepy?” Information Week, 12/15/2014, ref. http://www.informationweek.com/big-data/big-data-analytics/big-data-tool-analyzes-intentions-cool-or-creepy/d/d-id/1318128??itc=edit_in_body_cross%20

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AI Business Agents in Audits Universal purpose

To evaluate the integrity of internal controls which support

business decisions and governance

Types of audit

Financial statement – qualified vs. “clean”

IT – cybersecurity

Forensic – suspicion of criminal intent/activity

Audit standards are changing

Corporate – GAAP standard to COSO Framework

More structured evaluation process, driven by Sarbanes-Oxley

Government – Convergence to COSO Framework through

GAO endorsement

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AI Business Agents in Audits COSO Framework

Five Components of Internal Control (on front)

Committee of Sponsoring Organizations of the Treadway Commission(1980’s)

Voluntary private sector initiative

Endorsed by U.S. Government for agencies

Many variations

Highly amenable to AI treatment

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From U.S. Government Accountability Office, Standards for

Internal Control in the Federal Government, GAO-14-704G,

Published: Sep 10, 2014

AI Business Agents in Audits Beyond data mining

To recognize the difference between relevant and irrelevant data correlation

Phase 1 – BA as instrumentality to aid audit

Phase 2 – BA as intermediary/decision maker, in business process being audited

Can a BA be trained to deceive?

Asimov’s Laws. . . What then?

Liability issues and dispute resolution

The traceability of the machine recommendations (i.e., why a recommendation was made) will be important in fostering confidence and trust.1

1 Dr. Francesca Rossi, Professor of Computer Science, University of Padova and Harvard University, Your cognitive future: How next-gen computing changes the way we live and work, IBM Institute for Business Value, 2015; http://www-935.ibm.com/services/us/gbs/thoughtleadership/cognitivefuture/

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Conclusion

State of the art and business applications are moving fast in

many directions

Much yet to be done in AI development for procurement/SCM

Next frontier: SCM for services, especially at interface between

clerical and professional tasks

Expect regulation, at a minimum, escalating to changes in

legal environment

Labor displacement

Trust issues

Business and professional societies had better catch up, or they

will lose the initiative in regulatory process

NCMA, ISM, APS, SCMA . . . .

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Sources

Oxford University

Press, 2014

W.W. Norton &

Company, 2014

15

Sources

The University of

Michigan Press,

2011

Cambridge University

Press, 2014

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Q&A17