Artificial Intelligence and data science are increasingly being used to power decision making in settings that are highly consequential for people and society. Algorithmic decision making promises great benefits across sectors and domains. At the same time, there are many high-profile stories about the potential harms of algorithms, for individuals, population groups, and society at large. With great power comes great responsibility. What does “responsibility” mean from a technical perspective? What are technical solutions available to those who develop and deploy AI, machine learning and data science to help mitigate the potential risks of algorithms?
The concepts we develop in Fairness, Accountability, Confidentiality, Transparency in Artificial Intelligence (FACT-AI) are aimed at students who have joined or are likely to join the developer community. They should help them to articulate what concepts like “responsibility” mean from a technical perspective, and to make informed decisions when assessing and addressing the potential risks of algorithms.
This course will provide an overview of recent algorithmic approaches to improve fairness, transparency and privacy in AI. We will also discuss how such methods can be combined to improve accountability in algorithmic systems. We will discuss the technical challenges of implementing fairness, transparency and privacy techniques, as well as the challenges of anticipating how effective, in the long-run, such interventions are. We focus on both supervised learning and more recent systems such as LLMs and Agentic systems. Students will also discuss the environmental impacts of AI models and the societal challenges associated with recent generative AI methods.
This course will have a strong practical component: students will be exposed to state-of-the-art approaches and replicate/reproduce/extend a recently published paper in the area.