Ethical and Social Aspects of Artificial Intelligence

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Mail
ia@cs.upc.edu
This course covers AI ethical, legal, socioeconomic, cultural and gender practices, including fairness, accountability, user data rights, value alignment, and explainability. The frame is the actual practice in Europe.

Teachers

Person in charge

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Transversals

  • CT2 - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
  • CT4 [Avaluable] - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
  • CT8 [Avaluable] - Gender perspective. An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

    • Basic

  • CB1 - That students have demonstrated to possess and understand knowledge in an area of ??study that starts from the base of general secondary education, and is usually found at a level that, although supported by advanced textbooks, also includes some aspects that imply Knowledge from the vanguard of their field of study.
  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.

    • Technical Competences

    Especifics

  • CE15 - To acquire, formalize and represent human knowledge in a computable form for solving problems through a computer system in any field of application, particularly those related to aspects of computing, perception and performance in intelligent environments or environments.
  • CE16 - To design and evaluate human-machine interfaces that guarantee the accessibility and usability of computer systems, services and applications.
  • CE17 - To develop and evaluate interactive systems and presentation of complex information and its application to solving human-computer and human-robot interaction design problems.

    • Generic Technical Competences

    Generic

  • CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.
  • CG6 - To identify opportunities for innovative applications of artificial intelligence and robotics in constantly evolving technological environments.
  • CG7 - To interpret and apply current legislation, as well as specifications, regulations and standards in the field of artificial intelligence.
  • CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
  • CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

    • Objectives

    1. To explore the role of ethics in artificial intelligence practice and research
      Related competences: CG5, CG8, CT2, CT8, CB1, CB3, CE15, CE16, CE17,
    2. Be able to develop a set of ethical, legal, socioeconomic, cultural and gender criteria for the development of AI applications and evaluate each of the identified applications against these criteria.
      Related competences: CT4, CT8, CB3, CE15, CE16,
    3. To determine the AI technologies, tools, architectures, and algorithms that would be most suitable for the Industrial applications.
      Related competences: CG5, CG6, CG7, CG8, CG9, CT4, CT8, CE15, CE16, CE17,

    Contents

    1. Introduction. ethical principles
      In this module, we will consider the laws, policies and ethical principles for regulating and managing the use of AI for the common good.
    2. Applications of Artificial Intelligence for Health and other relevant domains:
      In this module, we will explore AI's current and emerging applications in health care and other relevant domains. Their legislation and ethical aspects. In particular, we will address the responsible decision-making procedures.
    3. Building an ELSEC approach to the use of AI
      This module will discuss how measures other than law and policy can ensure that AI improves human well-being.
    4. Applications of Artificial Intelligence
      En aquest mòdul, explorarem les aplicacions actuals i emergents de la IA en diverses aplicacions de domini. Les sol·licituds seran la base per a un debat en profunditat i el material a avaluar

    Activities

    Activity Evaluation act


    Introduction. Ethical principles


    • Theory: Introduction to Ethics and its philosophical background. What is technology ethics? Technology ethics is the application of ethical thinking to the practical concerns of technology.
    • Problems: In this module, we will develop a framework that defines the basic conditions for accountability throughout the entire AI life cycle - from responsible design to deployment.
    • Autonomous learning: Readings
    Objectives: 1
    Contents:
    Theory
    8h
    Problems
    8h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    24h

    Applications of artificial intelligence for healthcare


    • Theory: In this module, we address some potential ELSEC implications of AI-based systems in healthcare. Addressing ELSEC issues in healthcare can help to promote patient safety, privacy, and autonomy, as well as prevent unintended consequences or bias in the use of AI systems.
    • Problems: Directed discussions during class.
    • Autonomous learning: Readings
    Objectives: 3 1
    Contents:
    Theory
    2h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Building an ELSEC approach to the use of AI


    • Theory: In this module, we develop a framework that defines the basic conditions for accountability throughout the entire AI life cycle - from responsible design to deployment.
    • Problems: Directed discussions during class.
    • Autonomous learning: Readings
    Objectives: 3 2 1
    Contents:
    Theory
    6h
    Problems
    6h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    18h

    Artificial Intelligence applications.


    • Theory: Ethical dilemmas in AI: An introduction to the philosophical study of ethical dilemmas in AI. We will discuss in depth six actual technological applications that may be either legally or ethically problematic.
    • Problems: Directed discussions during class.
    • Guided learning: Readings
    Objectives: 3 2 1
    Contents:
    Theory
    12h
    Problems
    12h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    36h

    Conclusions


    • Theory: In this module, we conclude our course on ELSEC aspects of AI. We will reflect on the ethical, legal, social, and economic implications of AI and consider the values and principles outlined in the UNESCO Recommendation on the Ethics of Artificial Intelligence
    • Problems: N/A
    • Autonomous learning: Readings
    Objectives: 2
    Contents:
    Theory
    2h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Teaching methodology

    Actively engaging students. This can involve inviting students to surface their current ideas about ethics or engage in ethical decision-making activities before the lecturer presents the content to them for the first time.

    The teacher selects texts to match the needs of the course.

    We will use these methods to bring ELSEC thinking into the classroom, lectures, using case-studies, role-playing, and group discussions.

    Evaluation methodology

    We will ask all participants¿through an evaluation questionnaire¿to what extent they believe the seminar met its objectives.

    The course will be assessed by submitting three assignments, with proportional weighting. One of these assignments will be team-based, and at least one will be individual. These assignments will integrate the course learnings and allow each participant to demonstrate their ability to apply concepts to a practical case or real-world problem.

    The assignments' topics will be updated in each edition of the course to ensure their relevance and adaptability to the development of AI and its applications.

    Students will receive detailed feedback on each submitted assignment, highlighting strengths and areas for improvement.

    Brief consultation or tutoring sessions will be available to address questions and support participants in developing their assignments.

    Transparency and Communication:
    Before starting the course, the evaluation system will be explained to all students.

    Bibliography

    Basic

    Previous capacities

    The course has no prerequisites, but solid Elements of AI and logic.
    Reading Aristotle's Nicomachean ethics is recommended