Artificial Intelligence for an Innovation Driven Society (2235.YR.015136.1)

General information

Type:	OPT	Curs:	3,4	Period:	S semester
ECTS Credits:	2 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 3	Queralt Prat Pubill	Ciencias Sociales	ENG

Group	Teacher	Department	Language
Year 4	Queralt Prat Pubill	Ciencias Sociales	ENG

In March 2020, UK school exams were cancelled due to Covid-19. The government decided to use an automatic grading system in substitution of the cancelled exams. This solution lowered the grades of thousands of students. As a consequence, in August 2020, 200 students started protesting with signs such as: "Stop stealing our future?, "Grade my work, not my postcode?, "Fuck the algorithm?and "students not stats?. Ultimately, the artificial intelligence system was scrapped and replaced by grades predicted by teachers. This is just one example of the use of artificial intelligence in our society.

Artificial Intelligence is becoming increasingly complex, hard to understand, hard to predict and thus unable to control with existing social and organisational systems. In this course we explore the new required mindset to create the appropriate collaborative systems so artificial intelligence is managed in such a way as to favour innovation and protect against its many downside effects.

Any organisation needs to rethink how to organise itself in view of the continuous and dynamic changes created by innovation. New business models, access to new data, decisions made with artificial intelligence and new realities mean that rules, processes and systems become rapidly obsolete. For this reason, new multi-stakeholder governance models need to be designed, developed and tested because we have never had in human history the possibility of the creation and development of infinitely everchanging cyber-physical spaces in which we live.
Artificial intelligence is already permeating human societies. Managers need to understand the potential and risks of artificial intelligence and also know how to develop agile governance. A key insight is that multi-stakeholder collaboration needs to be procured and this comprehension means that organisations are going to become increasingly accountable not only in economic terms, but also socially and politically.
Leaders will have to create the appropriate value projects able to persuade, engage and cohere collectives, thus being able to use artificial intelligence for the benefit of human communities.
One of the critical hurdles to fostering innovation with AI in organizations is the leader's proficiency in effectively articulating and communicating the value of innovation. Simultaneously, leaders must also develop suitable organizational processes that encourage members to embrace a mindset shift and cultivate motivation for innovative work in the ever-evolving landscape of cyber-physical spaces intertwined with artificial intelligence.

By addressing these challenges and nurturing a culture that embraces innovation in the realm of AI, leaders can pave the way for organizations to effectively harness the potential of artificial intelligence and drive meaningful progress in their respective industries.

Artificial intelligence (AI) is rapidly becoming an integral part of decision-making processes in both business and government, greatly impacting our daily lives. From the pervasive content recommendation algorithms to the recruitment procedures, these automated decision systems are utilized in various domains such as government resource allocation and life-altering determinations like granting bail.

Despite the growing prevalence of AI systems, many managers possess limited knowledge about their inner workings, which hinders their ability to effectively implement these systems and leverage their potential for innovation while managing their potential pitfalls. Like any human creation, AI systems can be susceptible to errors and biases. However, in order to foster a thriving environment for innovation, it is essential to comprehend and effectively address these issues.

This course aims to empower participants with comprehensive knowledge about artificial intelligence systems, enabling them to cultivate a profound understanding of how AI can be effectively deployed in the business context and its far-reaching implications. By doing so, the course seeks to provide managers with the necessary skills to drive innovation through AI, while also fostering an understanding of its benefits and limitations. Moreover, the course emphasizes the importance of exercising proper control over the consequences that may arise from AI implementation, ensuring responsible and ethical practices throughout the process.

- Understanding key characteristics of Artificial Intelligence
- Understanding the risks and consequences of different types of artificial intelligence deployments
- Articulating designing principles for artificial intelligence to foster innovation and diminish negative side effects.

1. Course Structure There are 6 sessions planned: Module 1: Setting the stage Session 1. Organising for innovation. Artificial intelligence in our lives. Session 2. AI fundamentals Session 3. AI functionality Module 2: Case studies Session 4. Cases: Autonomous vehicles, health, education Session 5. Cases: Warfare, democracy Session 6. Wrap up

Burkov, A. (2020). Machine learning engineering.True positive Inc.
European Parliament. (2020). The ethics of artificial intelligence issues and initiatives : study Panel for the Future of Science and Technology (Issue March). (pages 53 to 65)
Littman, M. et al. (2021). Gathering strength, gathering storms. The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report (Issue September). http://ai100.stanford.edu/2021-report
Russell, S., & Norvig, P. (2022). Artificial Intelligence. A modern approach. Pearson.

Session 4
Bram, B. (2022). My Therapist, the Robot. The New York Times, 20-25.
Rathnayaka, P., Mills, N., Burnett, D., De Silva, D., Alahakoon, D., & Gray, R. (2022). A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring. Sensors, 22(10). https://doi.org/10.3390/s22103653
Aylett, M. P., Marsella, S., & Scott-morgan, P. (2022). Peter 2.0: Building a Cyborg. 169-175
Knight, W. (2022). Self-Driving Vehicles Are Here ? If You Know Where to Look. Wired Magazine, 1-5.
Marshall, A. (2022). Ford Abandons the Self-Driving Road to Nowhere. 1-5.
Marshall, A. (2023). Robot Cars Are Causing 911 False Alarms in San Francisco. Wired Magazine, 1-6.
Metz, C. (2022). The Long Road to Driverless Trucks. The New York Times, 22-25.

Session 5
Farrow, R. (2022). The surveillance states. The New Yorker.
Harari, Y. N. (2018, October). Why technology favors tyranny. The Atlantic.

Group	Teacher	Department
Year 3	Queralt Prat Pubill	Ciencias Sociales

Group	Teacher	Department
Year 4	Queralt Prat Pubill	Ciencias Sociales