Course Title: The AI Professional

Part A: Course Overview

Course Title: The AI Professional

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 1 2020,
Sem 1 2021


City Campus


175H Computing Technologies


Sem 1 2023,
Sem 1 2024


RMIT University Vietnam


175H Computing Technologies


Viet2 2022,
Viet2 2023

Course Coordinator: Professor Feng Xia

Course Coordinator Phone: -

Course Coordinator Email:

Course Coordinator Location: -

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities


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 Intelligence:

PLO1: Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.

PLO2: Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.

PLO3: Cognitive and Technical Skill - Critically analyse and evaluate user requirements and design systems employing software development tools, techniques, and emerging technologies.

PLO4: Communication - Communicate effectively with diverse audiences, employing a range of communication methods in interactions to both computing and non-computing personnel.

PLO5: Collaboration and Teamwork - Demonstrate effective teamwork and collaboration by using tools and practices to manage and meet project deliverables.

PLO6: Responsibility and Accountability - Demonstrate integrity, ethical conduct, sustainable and culturally inclusive professional standards, including First Nations knowledges and input in designing and implementing computing solutions.

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

Course Learning Outcomes (CLOs)

Upon successful completion of this course you should be able to:

  1. Apply relevant standards, ethical, social, privacy, and governance considerations, and thus demonstrate an understanding of issues related to the practice of an AI and Data Science professional.
  2. Analyse and discuss social impact and professional issues related to AI and Data Science (DS) and deployment of AI and DS systems. In particular, evaluate the implications of delegating control and decision making to AI and DS systems, including issues on fairness, bias, transparency, accountability and explainability of AI and DS systems
  3. Analyse and evaluate professional practice case studies, and to assess the work of peers
  4. Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations
  5. Use research principles and choose appropriate methods to analyse, theorise and justify conclusions about new situations in AI and DS 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)
Weighting: 20%
This assessment task supports CLOs 1,2,3,4,5

Assessment Task 2: AI/Data Science for social good project (group)
Weighting: 45%
This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment Task 3: Interviews and reflections (individual)
Weighting: 30%
This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment Task 4: Micro-Credentials
Weighting: 5%
This assessment task supports CLOs 2, 4