| CODE | ARI3900 | ||||||||
| TITLE | Ethics and Artificial Intelligence | ||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 6 | ||||||||
| ECTS CREDITS | 5 | ||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||
| DESCRIPTION | Mature students in their final year of the undergraduate degree in Artificial Intelligence (AI), as well as, graduate students switching to the area of AI, should possess the tools needed to reason and make decisions about the ethical, social and legal aspects of AI. In this unit the following topics will be covered: 1. Ethics and AI - motivating scenarios: self-driving cars, drones, bias in ML algorithms - ethical paradigms related to AI - artificial agents and responsibility - types of artificial agents - building of responsible agents 2. Algorithmic fairness - definitions of fairness in ML - motivating scenarios - enforcing fairness in ML models - practical work on analyzing bias in ML datasets - algorithmic fairness methods 3. Law and AI - outline of laws and how they operate in practice: GDPR - key terms and risks under GDPR - GDPR rules related autonomous decision making and profiling - other legal policies like the Anti-discrimination law, IPR, Compliance, and the MDIA act Study-Unit Aims: - The study-unit aims to give students the tools needed to reason and make decisions about the ethical, social and legal aspects of Artificial Intelligence (AI) through the in depth treatment of ethics in AI, algorithmic fairness in Machine Learning, and the Legal Aspects associated with AI. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Recognize the need of ethical standards in relation to the application of Artificial Intelligence when performing research as well as creating software artefacts; - Define which ethical aspects are of concern and decide what decisions need to be taken to ensure there are no ethical breaches; - Describe different social situations that could potentially create philosophical dilemma when artificial intelligent software is involved; - Identify any algorithmic fairness issues that can potentially arise when smart software is employed that could comprise bias especially in the use of particular data sets; - Recall specific laws and how they operate in practice related to the use of artificial intelligence and responsibility, accountability and ethics; - Review the key roles, terms and risks under the GDPR and other legal structures that include intellectual property, compliance, etc.; - Discuss the relevance of related local and European policies and laws regarding the use of artificial intelligence and machine learning. 2. Skills: By the end of the study-unit the student will be able to: - Evaluate the ethical and social implications of developments in machine learning and artificial intelligence and critique the technology of autonomous systems; - Incorporate ethical principles of the key ethical frameworks into the design of artificial agents, according to standard methodologies; - Analyse the social, ethical and legal (particularly data protection) barriers to the take-up of AI/ML technologies, including under the GDPR; - Assess the issues relevant to GDPR-compliant ML technology design and the consequences of non-compliance with legislation such as the GDPR; - Detect algorithmic bias in machine learning decisions and measure it based on several common metrics; - Reason about and apply the accuracy-fairness trade-off of machine learning models; - Evaluate appropriate algorithmic fairness measures to address the bias depending on the task, choose among pre-, in-, or post-processing methods, and perform empirical analysis using appropriate libraries. Main Text/s and any supplementary readings: Main Texts: - Kefi, Hajer. Information Technology Ethics. Newcastle-upon-Tyne: Cambridge Scholars, 2015. Web. (Available at library) - Oxford Handbook of Ethics of AI (not available in the library) by Markus Dubber (Author), Frank Pasquale (Author), Sunit Das (Author) ISBN-13 : 978-0197601440 2020 - Different content and media found on the web will be recommended. Supplementary Readings: - Kernaghan, Kenneth. "Digital Dilemmas: Values, Ethics and Information Technology." Canadian Public Administration 57.2 (2014): 295-317. (Available Online at: https://onlinelibrary.wiley.com/doi/full/10.1111/capa.12069) - Ethics of Artificial Intelligence 1st Edition (not available in the library) by S. Matthew Liao ISBN-13 : 978-0190905040 2020 |
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| STUDY-UNIT TYPE | Lecture, Ind Study & Ind Online Learning | ||||||||
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| LECTURER/S | Vanessa Camilleri Mireille-Martine Caruana Alexiei Dingli Matthew Montebello (Co-ord.) Ioannis Revolidis Dylan Seychell |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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