There is a clear and increasing interest in the ethical and societal implications of machine learning and artificial intelligence (AI). The goal of this workshop is to discuss:

Algorithmic Fairness and Bias Mitigation: Developing methods to ensure that AI systems operate without bias, providing fair outcomes across diverse populations. This includes understanding and correcting biases that may arise in data and algorithms.
Ethics of AI and Machine Learning: Exploring the moral principles guiding the development and deployment of AI technologies, ensuring they align with societal values and human rights. This encompasses discussions on the responsibilities of both public and private sectors in preventing discrimination through machine learning systems.
Transparency and Accountability in AI Systems: Advocating for clear and understandable AI decision-making processes to build trust and allow for effective oversight. This involves creating systems that are transparent about their operations and can be held accountable for their outcomes.
Regulatory and Policy Implications of AI: Analyzing how laws and regulations can keep pace with AI advancements to protect individuals and society, including discussions on industry standards and the role of governance in ensuring fair AI practices.
Human Rights and AI: Investigating how AI impacts fundamental human rights, such as privacy, equality, and non-discrimination, and proposing frameworks to safeguard these rights in the age of AI. This includes initiatives like the Toronto Declaration, which emphasizes protecting the rights to equality and non-discrimination in machine learning systems.

Confidence and Fairness: Scientific Foundations in AI and Risk