Tactical Development of AI (Executive Management)

Question 1

LLMs?

Answer 1

large language models

raise productivity

Question 2

ai management system?

Answer 2

tool developed to implement, operate, supervise & improve AI technical capabilities within an organisation

Question 3

key objectives senior executives should pursue when running ai management systems?

Answer 3

fairness
security
privacy
robustness
transparency
accountability
availability
maintainability
data quality
transparency & explicability

Question 4

is appropriate degree of ai expertise important in an organisation deploying ai systems?

Answer 4

yes

Question 5

human resources

Answer 5

hr planning & staffing requirements which are needed to use ai systems should be properly documented

Question 6

common positions involved in the development & operation of ai systems?

Answer 6

data scientists

specialist supervisors

specialist researches

database engineers

technically orientated customer service staff

Question 7

requirements to work in a technical capacity on an ai project

Answer 7

uni degree in quantitative discipline & relevant experience

Question 8

data analysis tools

Answer 8

python, r SAS, SQL

Question 9

examples of technology required for an AI system?

Answer 9

relational databases
uses an API
running on a GPU computer
uses cloud computing solutions
data visualisation apps

Question 10

impact assesment?

Answer 10

to determine the effect of an ai system on individuals and larger society

via assessing the consequences of deploying the system

Question 11

specific aspects of ai impact assessment

Answer 11

consider the context in which it’s used

the specific components of an assessment

staff must be qualified, available and non-conflicted

management make decisions

determine who stakeholders of the ai system are

Question 12

main technical resources required for ai project

Answer 12

data resources
software tools
hardware
back-up/disaster recovery

Question 13

steps in the ai system lifecycle

Answer 13

determining & documenting objectives

creating method as to how they can be achieved

prioritising the objectives

ascertaining how this may affect various parts of the development phase

Question 14

ANN?

Answer 14

artificial neural network

Question 15

other actions & considerations related to the AI lifecycle

Answer 15

• determining which stages require impact assessments
• choosing tolerances for testing the model & the methodology
• current level of expertise
• approval process at each stage
• rule changes
• ensuring maintainability
• ensuring maintainability
• setting procedures for monitoring & continuous improvement
• engagement with the system stakeholders

Question 16

technical considerations which go into the development phase of an ai system’s lifecycle

Answer 16

machine learning technique

algorithms

how the model’s trained/tested

hardware needed

non-machine learning model software

whether there are likely security threats

how outputs are presented/distributed

degree of human interaction

compatibility with other systems

Question 17

when is deciding the type/degree of oversight important?

Answer 17

when an ai system has the potential to impact individuals & societies - not just interacting with other technologies

Question 18

management must consider what concerning data for ai systems

Answer 18

sources of data

frequency & timeliness of data

different time periods of groupings

how data is collected

qualitative categories of data

quality

how data will be used

how data is pre processed

Question 19

data used in an ai system could come from

Answer 19

open sources
specialised vendors
organisation’s own customers
anywhere else which can generate business insights

Question 20

important elements to be shared with interested parties

Answer 20

reasons for using ai

fact that not interacting with a human, but ai

how to effectively interact with the system

technical requirements for using the system

key items from impact assessment

contact info

instructional material