Course Title: The AI Professional

Part A: Course Overview

Course Title: The AI Professional

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2778

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2020,
Sem 1 2021

Course Coordinator: Prof. Flora Salim

Course Coordinator Phone: +61 3 9925 0291

Course Coordinator Email: flora.salim@rmit.edu.au

Course Coordinator Location: 014.08.13

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

None


Course Description

We live in an era of rapid technological change which will increasingly see intelligent/data-driven computer systems take over control and decision making in diverse areas from business, law and security to healthcare and food production. This blended style course aims to equip you with the philosophical and ethical foundation needed by AI/Data Science professionals who will be at the forefront of transformation through AI and Data Science.

The role of an AI Professional and Data Scientist is an increasingly ubiquitous job role in many organisations of varying size. As well as a variety of technical skills, the practicing AI/Data Scientist also requires knowledge and awareness of key ethical, privacy and governance considerations for managing data, and data security and related techniques for implementing such. Moreover, a Data Scientist in an organisation must be an effective communicator both with other business units and with management, displaying an ability to both understand business needs and to communicate analyses effectively to inform decision making.

This course will: introduce concepts related to legal, ethical, privacy, governance issues for data collections; describe techniques for effectively managing such issues; provide you with techniques for effective communication and other considerations required of the AI practitioner or Data Scientist in a professional organisation.

The course will also: introduce the history of AI and Data Science, fundamental philosophical issues; consider current ethical issues in AI and building a responsible data science practice; explore the wider legal, social and economic impacts of widescale deployment of intelligent systems, and teach students to be innovative thinkers, leveraging the available datasets for social good.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

The course contributes to the program learning outcomes for MC271 – Master of Artificial Intellegence:

 

PLO 1: Enabling Knowledge

  • Demonstrate mastery of a body of knowledge that includes recent developments in Artificial Intelligence, Computer Science and information technology
  • Understand and use appropriate and relevant, fundamental and applied AI knowledge, methodologies and modern computational tools
  • Recognise and use research principles and methods applicable to Artificial Intelligence

 

PLO 2: Critical Analysis

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements
  • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of AI problems

 

PLO 3: Problem Solving

  • Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification
  • Apply an understanding of the balance between the complexity / accuracy of the Artificial techniques used and the timeliness of the delivery of the solution

 

PLO 4: Communication

  • Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

 

PLO 5: Team Work

  • Work effectively in different roles, to form, manage, and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances, and differing levels of technical expertise.

 

PLO 6: Responsibility

  • Effectively apply relevant standards, ethical considerations, and an understanding of legal and privacy issues to designing AI software,  applications and IT systems
  • Reflect on experience and improve your own future practice
  • Locate and use data and information and evaluate its quality with respect to its authority and relevance

 

PLO 7: Research and Scholarship

  • Demonstrate mastery of theoretical knowledge and to reflect critically on theory and professional practice or scholarship
  • Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship


Upon successful completion of this course you should have gained an understanding of the philosophical, ethical and legal issues related to AI system deployment. You should be able to:

  • CLO1 Apply relevant standards, ethical and social considerations, and thus demonstrate an understanding of issues related to the practice of an AI professional.
  • CLO2 Analyse and discuss social impact and professional issues related to AI and deployment of AI systems. In particular, evaluate implications of delegating control and decision making to AI systems.
  • CLO3 Analyse and evaluate professional practice case studies, and to assess the work of peers
  • CLO4 Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations
  • CLO5 Use research principles and choose appropriate methods to analyse, theorise and justify conclusions about new situations in AI professional practice and/or research


Overview of Learning Activities

The learning activities included in this course are:

  • Key concepts will be provided online (mix of text, audios, videos).
  • Workshop sessions are interactive and discussion based, where students will be put into small groups to analyse existing AI and Data Science case studies.
  • There will also be group presentation and peer review during the workshop sessions. These allow exploration of concepts with teaching staff and peers, and give feedback on students' progress and understanding.
  • Students are expected to go through the given materials before the lectorials, as well as conducting self-study to work through and research beyond the course as presented in classes and learning materials.
  • Assignments, as described in Overview of Assessment, requiring an integrated understanding of the subject matter.


Overview of Learning Resources

You will be able to access course information and learning materials and any recommended textbooks online linked through the Canvas learning management system. The course is divided into 12 weeks. The materials will be made available to you through Canvas each week prior to the classes. Lists of relevant reference texts, resources in the library and freely accessible online materials will also be provided.


Overview of Assessment

The assessment for this course comprises individual and group assignments, including a major project, presentations and peer reviews, as well as a list of Micro Creds to complete.Each of the assessment items requires you to demonstrate your knowledge of theoretical concepts, including application to new situations. Additionally, the assignments require you to research a scenario/problem, analyse it based on a professional framework, evaluate solutions, apply relevant standards, and create a professional report. Peer review involves oral and written feedback on the presentations delivered by other teams, as well as report(s) written by other students. The final project requires students to ideate, develop, and present an AI/Data Science-based idea and approach to a grand challenge faced by the society.

Note: This course has no hurdle requirements.

Assessment Task 1: AI/Data Science case study (group) – 20%
In this task, students will critically analyse and report (both oral presentation and written report) on an AI or DataScience case study, analysing the pitfalls of an existing AI system or digital solution. The assessment involves a presentation, a report, and peer review.  

This assessment task supports CLOs 1,2,3,4,5

Assessment Task 2: AI/Data Science for social good project (group) – 50%
In this task, students will critically analyse and report (both oral presentation and written report) on a chosen grand challenge faced by the society today. Students will apply design thinking and adopt an AI/Data science approach to solve the problem, focusing on AI for social good.The assessment involves a presentation, a report, and peer review, broken into two milestones: initial report, and final report and presentation.

This assessment task supportsCLOs 1, 2, 3, 4, 5

Assessment Task 3: Interviews and reflections (individual) – 25%
In conjunction with the group work (task 1 and 2), students need to submit a written reflection (5% each), with questions being given when they commence the task online. This will assess student’s contribution to their group project. At the end of the course, students will need to complete a recorded interview (15%) on practical case studies given to them. The recording will have a time limit.

This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment Task 4: Micro-Credentials -- 5%
The micro-credentials will ensure completion of essential mini courses on academic integrity, ethical cities, and presenting user story.

This assessment task supports CLOs 2, 4