We discuss the present and future of the hottest technology, AI, and its relationship to other important technologies such as supercomputing and quantum computing, among others. Possible futures raise fundamental questions that don't have simple answers: What will the social impacts be? What about the energy impacts? And the environmental impacts? Will these impacts depend on geography or other factors? 

Responsible AI covers mainly AI principles, governance & regulation, but most companies do not know how to implement all of these. Hence, in this presentation we cover the key questions for the whole process behind a new AI product, from the idea and design to the development and deployment. The questions are partly based on the new ACM Principles for Responsible Algorithmic Systems (2022) where I am one of the lead authors as well as their extensions for Generative AI (2023). For each question we will discuss its relevance, challenges, and solutions, triggering an interactive discussion.

In the first part, to set the stage, we cover irresponsible AI: (1) discrimination (e.g., facial recognition, justice); (2) pseudoscience (e.g., biometric based predictions); (3) limitations (e.g., human incompetence, minimal adversarial AI), (4) indiscriminate use of computing resources (e.g., large language models) and (5) the impact of generative AI (disinformation, mental health and copyright issues). These examples do have a personal bias but set the context for the second part where we address three challenges: (1) principles & governance, (2) regulation and (3) our cognitive biases. We finish discussing responsible AI initiatives and the near future.

Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is used well, ethically and scientifically. In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems.  The second part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, misinformation, and the impact on mental health. In the final part, we discuss regulation on the use of AI and responsible AI principles, that can mitigate the problems outlined above.

In this talk, we summarize how generative Artificial Intelligence (AI) works, its ethical and legal issues, and delve into the threats and challenges it presents to higher education and people's mental health. We complement this discussion with the first initiatives to regulate it and initial solutions to these problems.

We cover the evolution of search from lexical to semantic to the prediction-powered engines common today. We detail the technical mechanics of vectors, neural IR, and RAG. We also look at bigger philosophical AI questions: What are we losing when we let machines “guess” for us? Are we widening the digital divide? And are we getting a little too lazy?

In this presentation we cover all biases that affect search and recommender systems. They include biases on the data, the algorithms as well as the user interaction, particularly the ones related to relevance feedback loops (e.g., ranking and personalization).  In each case we cover the main concepts and when known, the techniques to ameliorate them, as well as biases that might be product of the evaluation methods used.

The Web is the most powerful communication medium and the largest public data repository that humankind has created. Its content ranges from great reference sources such as Wikipedia to ugly fake news. Indeed, social (digital) media is just an amplifying mirror of ourselves. Hence, the main challenge of search engines and other websites that rely on web data is to assess the quality of such data. However, as all people has their own biases, web content as well as our web interactions are tainted with many biases. Data bias includes redundancy and spam, while interaction bias includes activity and presentation bias. In addition, sometimes algorithms add bias, particularly in the context of search and recommendation systems. As bias generates bias, we stress the importance of debiasing data as well as using the context and other techniques such as explore & exploit, to break the filter bubble. The main goal of this talk is to make people aware of the different biases that affect all of us on the Web. Awareness is the first step to be able to fight and reduce the vicious cycle of web bias.