Human-Centric Artificial Intelligence

Recommended Prerequisites

Programming, mathematical, artificial intelligence and machine learning competencie.

Teaching Methods

Theoretical classes comprise detailed presentation of Human-Centered Artificial Intelligence concepts, principles and fundamental theories.
Practical Lab classes adopt a Project Based Learning approach, directed towards competence acquisition through the development of a laboratory work, comprising three components: (i) analysis of Human-Centered Artificial Intelligence works described in the literature, (ii) implementation, and (ii) writing of a scientific article, describing the lab work done, plus its presentation and defense.

Learning Outcomes

The goal of this Curricular Unit is to highlight the involvement of humans in Artificial Intelligence, which can happen in different manners, namely: humans are sources of data for AI models; humans might play an important role in the definition and construction of knowledge representation structures as well as in the the algorithms of reasoning/decision-making/learning of AI; humans are the main recipients of the output of the AI system. The student should become aware of the mechanisms that allow all the different components of the AI system to be re-traceable, explainable, understandable, open to human collaboration and, if needed, be made known to humans in a friendly, transparent and personalised way. The use of cognitive models in the construction of AI systems potentially favours the accomplishment of this goal.
The students are supposed to acquire knowledge and develop various skills such as analysis, synthesis, and critical thinking.

Work Placement(s)

No

Syllabus

1. Human-in-the-Loop
Motivation, Law, and Ethics
Humans as data, information, and knowledge producers
Collaborative Decision-making and Learning

2. Data Mining
Data set annotation
Lifelogging and autobiographical memories
Process mining
Text and opinion mining
Sentiment analysis
Crowdsourcing

3. Explainable and Interpretable AI
Interpretability and Explainability
Information Value Theory; Measuring Information
Causality
Explainable Models (e.g. Logistic and Linear Regression, Bayes Nets, Decision Trees, Decision Rules)
Neuro-symbolic learning
Trust and Privacy

4. Cognitive Systems
Cognitive and Behaviour Modeling
User and Group Modeling
Personalization
Cognitive Architectures

5. Recommender Systems
Techniques
Evaluation
Advanced Topics
Case Studies

6. Intelligent Personal Assistant Agents

Head Lecturer(s)

Fernando Amílcar Bandeira Cardoso

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. Pearson, Upper Saddle River, NJ, 2009.

D. Koller, N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, MA, 2009.

A. Holzinger, P. Kieseberg, E. Weippl & A Min Tjoa. Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI. Springer Lecture Notes in Computer Science LNCS 11015. Cham: Springer, pp. 1-8, 2018.

C. Molnar. Interpretable machine learning. A Guide for Making Black Box Models Explainable, 2019. https://christophm.github.io/interpretable-ml-book/.

T. Miller.  Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38, 2019.

F. Ricci, L. Rokach, B. Shapira. Recommender Systems Handbook. Springer, 2015.