Course Curriculam
Machine learning has been hastily operationalised, often with little regard for its wider societal impact. At the same time, there's been a lack of clear, concrete guidelines on how to reduce the risks stemming from AI. With that in mind, we have started a Non-profit organization, the Foundation for Best Practices in Machine Learning. Our goal is to help data scientists, governance experts, managers, and other machine learning professionals implement ethical and responsible machine learning. We do that via our free, open-source technical and organisational Best Practices for Responsible AI.
These guidelines have been developed principally by senior ML engineers, data scientists, data science managers, and legal professionals. The Foundation’s philosophy is that (a) context is key, and (b) responsible ML starts with prudent MLOps and product management.
The technical and organisational best practices look at both the technical and institutional requirements needed to promote responsible ML. Both blueprints touch on subjects such as “Fairness & Non-Discrimination”, “Representativeness & Specification”, “Product Traceability”, “Explainability” amongst other topics. Where the organisational guide relates to organisation-wide process and responsibilities (f.e. the necessity of setting proper product definitions and risk portfolios); the model guide details issues ranging from cost function specification & optimisation to selection function characterisation, from disparate impact metrics to local explanations and counterfactuals. It also addresses issues concerning thorough product management.
Instructors
Data Scientist ABN Amro Bank
Violeta Misheva
Violeta has been interested in understanding the causes of social inequalities and to what extent bad experiences early in life propagate to negative outcomes later. When she realised ML can result in widening already existing social gaps, she became an advocate for the responsible development and deployment of ML. Violeta currently works as a data scientist at ABN Amro. Before that, she worked in consultancy and obtained her PhD in applied econometrics. Violeta likes sharing her knowledge with others by the form of workshops on data science and online courses. Violeta proposes that developers of ML solutions alone cannot ensure their safety but, rather, that the additional efforts of multidisciplinary experts as well as proper regulation is also needed.These guidelines have been developed principally by senior ML engineers, data scientists, data science managers, and legal professionals. The Foundation’s philosophy is that (a) context is key, and (b) responsible ML starts with prudent MLOps and product management.
The technical and organisational best practices look at both the technical and institutional requirements needed to promote responsible ML. Both blueprints touch on subjects such as “Fairness & Non-Discrimination”, “Representativeness & Specification”, “Product Traceability”, “Explainability” amongst other topics. Where the organisational guide relates to organisation-wide process and responsibilities (f.e. the necessity of setting proper product definitions and risk portfolios); the model guide details issues ranging from cost function specification & optimisation to selection function characterisation, from disparate impact metrics to local explanations and counterfactuals. It also addresses issues concerning thorough product management."
Legal Counsel for AI & Data Science
Daniel Vale
Daniel has long been interested in the intersection between the law, technology and society. Unsurprisingly, this drew him into the field of data science and law. Daniel currently works as legal counsel for AI & data science at the H&M Group: where his principal focus is on developing and maturing the company’s MLOps (business, governance, and regulatory) capacities. Daniel is also completing his PhD in law, MLOps, & finance at Leiden University. His education is in behavioural science, statistics, and law. Having worked at corporate law firms and as a consultant, Daniel has practical legal and commercial experience in the field. He proposes that responsible ML is centred around two essential themes - (a) a constant appreciation of context, and (b) prudent MLOps & project management.