Module Catalogue 2020/21

CSC8611 : Human-Artificial Intelligence (AI) Interaction & Futures

  • Offered for Year: 2020/21
  • Module Leader(s): Dr Jan Smeddinck
  • Lecturer: Dr Yu Guan
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0
Pre Requisites
Pre Requisite Comment

N/A

Co Requisites
Co Requisite Comment

N/A

Aims

Artificial Intelligence (AI) is redefining the way we live and work by enabling the design and development of automated processes that mimic human cognition and behaviour and provide deep and complex integration between information to respond autonomously. The ultimate goal of AI is to support human decision making and action with informed intelligent services. This course concerns critical and responsible design, development and evaluation of AI technologies with a focus on human-AI-interaction. The aim of this module is to provide students with a cross-disciplinary background and the advanced skills of utilising and critically evaluating the impact of Human-AI concepts and technologies within their ecosystems.

Outline Of Syllabus

•       Introduction to advanced automation (personalisation, adaptive systems, prediction/forecasting, cognitive services, qualitative analysis (visual and natural language processing), hybrid intelligence systems, black boxing)
•       Intelligence, problem solving & decision making in humans and machines
•       Designing interactions with applied artificial intelligence, machine learning (ML) & recommender systems
•       AI interaction and experience design + development
•       Human-AI benefits, victims & disasters
•       Understandable / relatable AI
•       Ethical & responsible AI
•       Human-AI ecosystems & markets (case studies e.g. in autonomous agriculture, manufacturing, transportation, finance, healthcare, security, social media, gaming ...etc)

Learning Outcomes

Intended Knowledge Outcomes

•       To have a broad foundational understanding of types and techniques in AI/ML
•       To be able to demonstrate good understanding of the potential use cases and benefits of artificial intelligence (AI) technologies
•       To have a critical understanding of the ethical, social and legal implications of AI applications on human life and work
•       To be able to understand appropriate design, development and research methods for human-AI interaction

Intended Skill Outcomes

•       To be able to design and develop applied artificial intelligence / machine learning applications for given requirements
•       To be able to critically assess potential benefits and possible negative effects of AI systems in situated use

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials360:3018:00Asynchronous online delivery (primarily video)
Guided Independent StudyDirected research and reading121:0012:00Preparatory reading & practice for taught sessions (accompanies asynchronous lec materials blocks).
Scheduled Learning And Teaching ActivitiesWorkshops61:006:00Synchronous online – timetabled workshops PiP. Practical aspects AI/ML interactive system developmnt
Guided Independent StudyProject work61:006:00Asynchronous online: Supported work on applied project & writeup- face to face with lec if poss
Guided Independent StudyProject work172:0034:00Project implementation & structured discussion/ reflection on learning objectives of coursework
Guided Independent StudyStudent-led group activity61:006:00Asynchronous online –linked to scheduled synchronous workshop activities
Guided Independent StudyIndependent study62:0012:00Background reading
Scheduled Learning And Teaching ActivitiesScheduled on-line contact time61:006:00Synchronous online - Live discussion, small group activities, feedback and Q&A on online asynch
Total100:00
Teaching Rationale And Relationship

The teaching methods provide a framework for the student to understand and investigate Human-AI applications for a given problem. Workshops with a synchronous and organised / student-group led component will deliver hands-on skills for employing a range of modern AI/ML methods based on common frameworks and exemplary data sets. The independent project work enables the student to immerse themselves in a research area to gain domain-related knowledge about applications of machine learning and artificial intelligence methods.

Reading Lists

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report2M100Structured discussion / reflection on key learning objectives of coursework project + applied AI implementation and evaluation repor
Formative Assessments
Description Semester When Set Comment
Written exercise2MOutline of project report. Feedback will be given prior to summative assessment
Assessment Rationale And Relationship

The report examines the learners’ ability to critically reflect on the design and development of human-AI applications for given requirements and against a specific application use-case. This is akin to real-world AI/ML applications / solutions development, but on a smaller (model) scale. The assessment tests the students’ ability to use key frameworks to explore applications of data analytics and/or machine learning and/or artificial intelligence methods in realistic contexts and improve the students’ professional portfolio and employability. An AI-user testing component built into the assignment will support reflective discussion about how individual students have met the learning objectives of the module and how the principles of professional practice in data science were embedded in the student’s practical experience.
The formative assessment will provide students with feedback on their ideas for their emerging project report.

Timetable

Past Exam Papers

General Notes

N/A

Disclaimer: The information contained within the Module Catalogue relates to the 2020/21 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2021/22 entry will be published here in early-April 2021. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.