| Module Code: |
H9HCAI |
|
Long Title
|
Human Centered Artificial Intelligence
|
|
Title
|
Human Centered Artificial Intelligence
|
| Module Level: |
LEVEL 9 |
| EQF Level: |
7 |
| EHEA Level: |
Second Cycle |
| Module Coordinator: |
Paul Stynes |
| Module Author: |
Shauni Hegarty |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Demonstrate expert knowledge of the theory and concepts underpinning human centered AI. |
| LO2 |
Determine the design requirements for human centered AI systems. |
| LO3 |
Critically analyse the capabilities and limitations of AI systems based on the governance structures of
the human centered AI. |
| LO4 |
Investigate and critically assess the impacts of reliability, trustworthiness, fairness, accountability, and
transparency in AI. |
| LO5 |
Evaluate and present the adherence of AI systems to the human centered AI guidelines |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
|
| No recommendations listed |
Co-requisite Modules
|
| No Co-requisite modules listed |
Module Content & Assessment
| Indicative Content |
|
Introduction to Human Centered Artificial Intelligence
• How do rationalism or empiricism provide
sound foundations?
• Are people and computers in the same
category?
• Will automation, AI, and robots lead to
widespread unemployment?
• Harnessing the benefits of emulating humans
and empowering people
• Trade-offs between emulating humans and
empowering people
|
|
Rising above the levels of automation
• How to design to safely increase human
performance?
• Understand the situations to apply full
human or full computer control
• Balance between human and computer
control
|
|
Two dimensional HCAI framework
•Introduction to two-dimensional HCAI
framework
• Categorisation of systems using the
framework
|
|
Design guidelines and examples
• Introduction to the HCAI guidelines
• Application of the guidelines to design HCAI
systems
• Example of systems developed using the
HCAI guidelines
|
|
Defining reliable, safe & trustworthy systems
• What means a reliable, safe, and trustworthy
system?
• What determines the reliability, safeness,
and trustworthiness of a system?
• How to measure reliability, safeness, and
trustworthiness of a system?
|
|
Governance Structures for HCAI
How to bridge the gap from ethicsto practice
• Introduction to the three-layer governance
structure for HCAI systems
• Application of the governance structure
|
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Reliable AI systems
Audit Trails and Analysis Tools
• Verification and Validation Testing
|
|
Trustworthy certification by independent oversight
• Introduction to oversight methods
• Government Interventions and Regulations
• Accounting Firms Conduct External Audits
• Insurance Companies Compensate for AI
Failures
• NGO, Professional Organisations, and
Research Institutes
|
|
Fairness in AI
• The meaning of fairness with respect to AI
• Perceptions of algorithmic bias and
unfairness
• Legal, social, and philosophical models of
fairness
• Methods, tools, and standards for ensuring
that algorithms comply with fairness policies
(e.g., IEEE P7003 TM)
• Mitigating biases in systems, or discouraging
biased behaviour from users
|
|
Accountability in AI
The meaning of accountability with respect
to algorithmic systems
• Strategies for developing accountable
systems
• Methods and principles for accountable
algorithms (e.g., FAT/ML Principles for Accountable Algorithms, Social Impact
Statement for Algorithms)
|
|
Transparency in AI
• The meaning of transparency with respect to
algorithmic systems
• Tools, models, and principles for AI
explainability and transparency (e.g., ACM
Principles for Algorithmic Transparency and
Accountability, NIST Principles of Explainable
AI)
• Trade-offs between privacy and
transparency
• Tools and Frameworks for conducting ethical
and legal algorithm audits
|
|
Human Centered Approach to AI Ethics
• Problemsin AI and robot ethicsthat arise due
to cognitive states
• Define what is welfare and responsibility
• Review of HCAI approach to resolve some of
ethics problems
|
| Assessment Breakdown | % |
|---|
| Coursework | 30.00% |
| End of Module Assessment | 70.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Formative Assessment |
% of total: |
Non-Marked |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
Yes |
Assessment Description:
Formative assessment will be provided on the
in-class individual or group activities. Feedback
will be provided in written or oral format, or
on-line through Moodle. In addition, in class discussions will be undertaken as part of the
practical approach to learning. |
|
| Assessment Type: |
Continuous Assessment |
% of total: |
30 |
| Assessment Date: |
n/a |
Outcome addressed: |
1 |
| Non-Marked: |
No |
Assessment Description:
Discuss the challenges an organisation faces
for adopting AI due to the differences between
humans and computers. How can HCAI
improve and enhance this experience? |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
70 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5 |
| Non-Marked: |
No |
Assessment Description:
The examination will be of two hours duration
and may include a mix of: theoretical, applied
and interpretation questions |
|
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
|
NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
| Module Target Workload Hours 0 Hours |
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Lectures |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent Learning |
202 |
Per Semester |
16.83 |
| Tutorial |
Practical/Tutorials |
24 |
Per Semester |
2.00 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Shneiderman, B. (2022). Human-Centered AI. Oxford University Press.
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Dubber, M. D., Pasquale, F., & Das, S. (Eds). (2020). The Oxford Handbook of Ethics of AI. Oxford University Press. [ISBN 978-0190067397]..
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O'Keefe, K. & O Brien, D. (2018). Ethical Data and Information Management. Kogan Page. [ISBN: 978- 0749482046]..
| | Recommended Article/Paper Resources |
|---|
-
Chrisley, R. (2020). A human-centered
approach to AI ethics: A perspective
from cognitive science. The Oxford
Handbook of Ethics in AI. Oxford
University Press. DOI:
10.1093/oxfordhb/9780190067397.013.29..
-
Shneiderman, B. (2020a). Human-centered
artificial intelligence: Three fresh
ideas. AIS Transactions on
Human-Computer Interaction, 12(3),
109-124. DOI: 10.17705/1thci.0013..
-
Shneiderman, B. (2020b). Human-centered
artificial intelligence: Reliable, safe
& trustworthy. International Journal
of Human-Computer Interaction,36(6),
495-504.
-
Shneiderman, B. (2020c). Design lessons
from AI’s two grand goals: Human
emulation and useful applications. IEEE
Transactions on Technology and Society,
1(2), 73-82.
-
Shneiderman, B. (2020d). Bridging the
gap between ethics and practice:
Guidelines for reliable, safe, and
trustworthy human-centered AI systems.
ACM Transactions on Interactive
Intelligent Systems.10(4), Article 26.
DOI: 10.1145/3419764.
-
Hassani, H., Silva, E. S., Unger, S.,
TajMazinani, M. and Mac Feely, S.
(2020). Artificial Intelligence (AI) or
Intelligence Augmentation (IA): What is
the future? AI, 1(2), 143-155. DOI:
10.3390/ai1020008.
| | This module does not have any other resources |
|---|
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