| Study programme competencies |
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Code
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Study programme competences
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| A6 |
CE05 - Ability to design and develop intelligent systems through the application of inference algorithms, knowledge representation and automated planning |
| A7 |
CE06 - Ability to recognise those problems that require a distributed architecture, not predetermined during the system design, suitable for the implementation of multiagent systems |
| A8 |
CE07 - Ability to understand the consequences of the development of an explainable and interpretable intelligent system |
| A9 |
CE08 - Ability to design and develop secure intelligent systems, in terms of integrity, confidentiality and robustness |
| B1 |
CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI |
| B2 |
CG02 - Successfully addressing each and every stage of an AI project |
| B3 |
CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B6 |
CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 |
CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B8 |
CB03 - The students will be able to integrate different pieces of knowledge, to face the complexity of formulating opinions (from information that may be incomplete or limited) and to include considerations about social and ethical responsibilities linked to the application of their knowledge and opinions |
| B9 |
CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities |
| C2 |
CT02 - Command in understanding and expression, both in oral and written forms, of a foreign language |
| C3 |
CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C4 |
CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective |
| C5 |
CT05 - Understanding the importance of the entrepreneurial culture and knowledge of the resources within the entrepreneur person's means |
| C6 |
CT06 - Acquiring abilities for life and healthy customs, routines and life styles |
| C7 |
CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 |
CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| Learning aims |
| Learning outcomes |
Study programme competences |
| Develop capacities for an adequate treatment of privacy, reliability, transparency and interpretability of models and results |
AC5
AC6
AC7
AC8
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BC1
BC2
BC3
BC6
BC7
BC8
BC9
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CC2
CC3
CC4
CC5
CC6
CC7
CC8
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| Identify and analyze biases and their impact on the design of Artificial Intelligence algorithms |
AC5
AC6
AC7
AC8
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BC1
BC2
BC3
BC6
BC7
BC8
BC9
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CC2
CC3
CC4
CC5
CC6
CC7
CC8
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| Know and understand the social and ethical implications of technology in general and Artificial Intelligence in particular |
AC5
AC6
AC7
AC8
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BC1
BC2
BC3
BC6
BC7
BC8
BC9
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CC2
CC3
CC4
CC5
CC6
CC7
CC8
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| Contents |
| Topic |
Sub-topic |
Explainability and interpretability. Model-agnostic methods. Explanations based on examples. FAT-E (fairness, accountability, transparency and ethics). Study and types of biases. Types and models of explanation. Evaluation methodologies. Data integrity, privacy, confidentiality and robustness
of models. Reliability by design
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Explainability and interpretability. Model-agnostic methods. Explanations based on examples. FAT-E (fairness, accountability, transparency and ethics). Study and types of biases. Types and models of explanation. Evaluation methodologies. Data integrity, privacy, confidentiality and robustness
of models. Reliability by design
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| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Laboratory practice |
A6 A7 A8 A9 B1 B2 B3 B6 B7 B8 B9 C2 C3 C4 C5 C6 C7 C8 |
11 |
43 |
54 |
| Guest lecture / keynote speech |
A6 A7 A8 A9 B1 B2 B3 B6 B7 B8 B9 C2 C3 C4 C5 C6 C7 C8 |
10 |
10 |
20 |
| |
| Personalized attention |
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1 |
0 |
1 |
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| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
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Methodologies |
Description |
| Laboratory practice |
The interactive classes will take place in the selected Computer Classroom at each University, using various software tools for each of the thematic blocks, addressing exercises and projects with different levels of complexity. The students will work in individual positions with the constant support of the teaching staff. The scripts of the practices will be self-explanatory, allowing students to take profit of their personal work hours. Practical classes are aimed for developing skills CG1, CG3, CB6, CB7, CB8, CT3, CT8, CE5, CE6, CE7, CE8, CE9.
Students develop practical work that involves dealing with the resolution of complex problems, and the analysis and design of solutions that constitute a means for their resolution. Students may have to present their work orally. The work done by the students can be done individually or in work groups.
Students can work on the solution to the problems raised individually or in groups. This teaching methodology will be applied to the training activity "Practical laboratory classes" and may be also applied to the training activity of "Problem-based learning sessions, seminars, case studies and projects".
Laboratory practices: the teaching staff of the subject poses to the students problems of a practical nature whose resolution requires the understanding and application of the theoretical-practical contents included in the contents of the subject.
Learning by projects: students are presented with practical projects whose scope requires an important part of the total dedication of the student in this subject. In addition, due to the scope of the work to be carried out, it is required that the students apply technical and non-technical skills.
Teaching will be supported by the virtual platform of the master in the following way: repository of documentation related to the subject (texts, presentations, etc.) and virtual tutoring of students (e-mail and forums).
Tutoring: the teaching staff will assist students in individualized tutorial sessions dedicated to study orientation and the resolution of doubts about the contents and work of the subject
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| Guest lecture / keynote speech |
The teaching methodology will be based on the individual work of the students, on the discussion with the teacher in class and on individual tutorials.
Theory classes (expository): Oral presentation complemented with the use of audiovisual media and the introduction of some questions addressed to students, in order to transmit knowledge and facilitate learning. In addition to the oral presentation, students should dedicate some time to prepare and review the class materials on their own.
For each theme or thematic module of the expository classes, the teaching staff will prepare the contents, explain the objectives of the theme to the students in class, present each theme with the aim of providing a set of information with a specific scope, suggest a bibliography, provide additional work material, etc. This teaching methodology will be applied to the training activity "Theory classes".
Theory classes are aimed for developing skills CG1, CG3, CB6, CB7, CB8, CB9, CE5, CE6, CE7, CE8, CE9. In addition, the teaching staff will propose to the students a set of activities to carry out, individually or in groups (case studies, papers, presentations, readings, etc.). Students must submit a selection of them for evaluation. As a result, students will develop the skills CG3, CB7, CB8, CB9, CT2, CT3, CT4, CT6, CT8, CE7, CE8
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| Personalized attention |
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Methodologies
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| Laboratory practice |
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| Assessment |
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Methodologies
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Competencies |
Description
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Qualification
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| Guest lecture / keynote speech |
A6 A7 A8 A9 B1 B2 B3 B6 B7 B8 B9 C2 C3 C4 C5 C6 C7 C8 |
exam of the theoretical part (45%) |
45 |
| Laboratory practice |
A6 A7 A8 A9 B1 B2 B3 B6 B7 B8 B9 C2 C3 C4 C5 C6 C7 C8 |
evaluation of the deliveries associated with the interactive sessions (35%), the delivery of a personal work and its oral presentation (15%) and the continuous assessment of each student throughout the course (5%) |
55 |
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Assessment comments |
The evaluation of the learning considers an exam of the theoretical
part (45%) and the evaluation of the deliveries associated with the interactive
sessions (35%), the delivery of a personal work and its oral presentation (15%)
and the continuous assessment of each student throughout the course (5%).
It is mandatory to pass all parts (exam, practices, work, continuous
evaluation), considering the following criteria:
1. Exam (45%): The theoretical content of the subject will be
evaluated in a single exam to be taken on the official date. The exam will
consist of questions related to all the topics of the program. The exam will be
specially oriented to evaluate the comprehension of the knowledge exposed in
the theory classes. The exam grade will be the weighted average of the modules
of the subject, which will only be calculated in the case of having a grade
equal to or greater than 4 in each module.
2. Practices (35%): There will be mandatory deliveries associated
with the interactive sessions related to each theoretical module. The solutions
proposed by the students to the proposed practices will be evaluated. The
evaluation of practices can be carried out through a correction by the teacher,
or a defense of the solution provided by the student in the form of an oral
presentation of the solution developed. (Applicable to the results of the
training activities "Practical laboratory classes",
"Problem-based learning, seminars, case studies and projects" and
"Carrying out supervised work"). The average grade will only be
calculated in the case of having a grade greater than or equal to 4/10 in all
deliveries. In addition, it is mandatory face-to-face attendance of at least
60% of the interactive classes.
3. Work (15%): Students must submit and present a personal work
according to the calendar established at the beginning of the semester. The
evaluation of the supervised work will be carried out by means of a defense in
which the students explain their proposal and conclusions to the teacher, or by
means of an oral presentation of the solution in front of the classroom. The
grade obtained will be the average of the evaluation of the written work and
its oral presentation. The average will only be made if a grade equal to or
greater than 4 is obtained in each part.
4. Continuous evaluation (5%): The attendance and active
participation of students will be taken into account in the expository classes but
also during the presentation of works, discussions, seminars, and in the
interactive sessions that are held throughout the course. It is mandatory attending
at least 60% of the presentation sessions and seminars.
The final grade for the subject will be the sum of the four partial
grades, except in those situations indicated above. When any part is not
passed, the final grade for the opportunity will be the minimum of the partial
grades.
Students who have not participated in any of the evaluation
activities will obtain the grade of not presented.
Students who have official exemption from class attendance must
take, in any case, the final written exam, as well as doing all deliveries of
practices and work that are established as mandatory throughout the course and,
if required, make their oral presentations. In this modality, the tutoring,
deliveries and oral presentations can be made remotely.
In the second opportunity, the students must pass the pending
evaluation activities of the first opportunity, in accordance with the previous
criteria.
For cases of fraudulent completion of exercises
or tests, the provisions of the Regulations for evaluating the academic
performance of students and reviewing grades will apply. The total or partial
copy of any practice or theory exercise will automatically mean a grade of 0.0
in the subject and opportunity
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| Sources of information |
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Basic
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Supplementary material will be provided in the virtual platform of the master to facilitate following each unit in this subject. Given the heterogeneity of topics to be dealt within this subject, with each class, references to bibliographic resources as well as other types of content (reports, multimedia, etc.) will be provided to student for the more specific aspects of the subject. The following references are of a complementary type, they deal with general aspects related to explainable and trustworthy AI. 1. V. Dignum. Responsible Artificial Intelligence. How to Developand Use AI in a Responsible Way. Springer Nature Switzerland AG, 2019, ISBN: 978-3-030-30370-9 , https://doi.org/10.1007/978-3-030-30371-6 2. A. Barredo Arrieta et al., Explainable Artificial Intelligence(XAI): Concepts, taxonomies, opportunities and challenges toward responsibleAI, Information Fusion, 58:82-115, Elsevier 2020, https://doi.org/10.1016/j.inffus.2019.12.012 3. T. Miller, Explanation in artificial intelligence: Insights fromthe social sciences. Artificial Intelligence, 267:1-38, Elsevier 2019, https://doi.org/10.1016/j.artint.2018.07.007 4. G. Vilone, L. Longo, Notions of explainability and evaluationapproaches for Explainable Artificial Intelligence, Information Fusion,76:89-106, Elsevier 2021, https://doi.org/10.1016/j.inffus.2021.05.009 5. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti,D. Pedreschi, A Survey of Methods for Explaining Black Box Models, ACMComputing Surveys, 51(5):1–42, 2019, https://dl.acm.org/doi/10.1145/3236009 6. J.M. Alonso, C. Castiello, L. Magdalena, C.Mencar, Explainable Fuzzy Systems. Paving the way from interpretable fuzzysystems to explainable AI systems. SpringerInternational Publishing, 2021, ISBN: 978-3-030-71098-9, https://doi.org/10.1007/978-3-030-71098-9 |
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Complementary
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| Recommendations |
| Subjects that it is recommended to have taken before |
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| Subjects that are recommended to be taken simultaneously |
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| Subjects that continue the syllabus |
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| Other comments |
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It
is recommended to bring the subject up to date and the use of tutoring sessions
to clarify doubts and get advise on its development. In addition, it is
recommended that students solve, verify and validate all the exercises and
practices proposed during the course (no matter if they are or not to be
officially evaluated) |
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