1. Open-source Best Practices in Responsible AI
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 characterization, from disparate impact metrics to local explanations and counterfactuals. It also addresses issues concerning thorough product management.
Speakers: Violeta Misheva, Ph.D., Senior Data Scientist and Vice-chair, ABN Amro Bank | The Foundation for Best Practices in ML and Daniel Vale, Legal Counsel for AI & Data Science, H&M Group
2. Trustworthy AI
Recent years have seen an astounding growth in deployment of AI systems in critical domains such as autonomous vehicles, criminal justice, and healthcare, where decisions taken by AI agents directly impact human lives.
Consequently, there is an increasing concern if these decisions can be trusted. How can we deliver on the promise of the benefits of AI but address scenarios that have life-critical consequences for people and society? In short, how can we achieve trustworthy AI?
Under the umbrella of trustworthy computing, employing formal methods for ensuring trust properties such as reliability and security has led to scalable success. Just as for trustworthy computing, formal methods could be an effective approach for building trust in AI-based systems. However, we would need to extend the set of properties to include fairness, robustness, and interpretability, etc.; and to develop new verification techniques to handle new kinds of artifacts, e.g., data distributions and machine-learned models. This talk poses a new research agenda, from a formal methods perspective, for us to increase trust in AI systems.
Speaker: Jeannette M. Wing, PhD, Avanessians Director, Data Science Institute & Professor of Computer Science, Columbia University
3.ImageNet and its Discontents. The Case for Responsible Interpretation in ML
Sociotechnical systems abound in examples of the ways they constitute sources of harm for historically marginalized groups. In this context, the field of machine learning has seen a rapid proliferation of new machine learning methods, model architectures, and optimization techniques. Yet, data -- which remains the backbone of machine learning research and development -- has received comparatively little research attention.
My research hypothesis is that focusing exclusively on the content of training datasets — the data used for algorithms to “learn” associations — only captures part of the problem. Instead, we should identify the historical and conceptual conditions which unveil the modes of dataset construction. I propose here an analysis of datasets from the perspective of three techniques of interpretation: genealogy, problematization, and hermeneutics.
First, genealogy investigates how datasets have been created and the contextual and contingent conditions of their creation. This includes questions on the role of data provenance, the conceptualization and operationalization of the categories which structure these datasets (e.g. the labels which are applied to images), methods for annotation, the consent regimes of the data authors and data subjects, and stakeholders and other related institutional logics.
Second, the technique of problematization builds on the genealogical question by asking: what are the central discourses, questions, concepts, and values which constitute themselves as the solution to problems in the construction of a given dataset.
Third, building on the previous two lines of inquiry, we have the hermeneutical approach, which is concerned with investigating the explicit and implicit motivations of all present and absent stakeholders (including data scientists and dataset curators) and the background assumptions operative in dataset construction.
Speaker: Razvan Amironesei, Ph.D., Applied Data Ethicist & Visiting Researcher, Google
4. Changing the Narrative: The Importance of Responsible AI and Human-AI Collaboration
While AI brings great potential and improves many aspects of our lives, it also raises concerns regarding employment, privacy, safety and many others. It is time to change the narrative from human / AI competition to human / AI collaboration. AI can be utilized to amplify human potential, to assist people in everyday life, from education, to manufacturing to supporting our most vulnerable population. However, to enable AI to venture into the real world and work closely with people, we need to develop contextually aware technologies that are built on robust and ethical AI. In this keynote, we will discuss Intel's research in different applications as well as robust and ethical perception utilizing multi-modal sensing and sensemaking and probabilistic computing.
Speaker: Lama Nachman, Intel Fellow, Director of Human & AI Systems Research Lab, Intel
5. Responsible AI; From Principles to Practice
AI has made amazing technological advances possible; as the field matures, the question for AI practitioners has shifted from “can we do it?” to “should we do it?”. In this talk, Dr. Tempest van Schaik will share her Responsible AI (RAI) journey, from ethical concerns in AI projects, to turning high-level RAI principles into code, and the foundation of an RAI review board that oversees projects for the team. She will share some of the practical RAI tools and techniques that can be used throughout the AI lifecycle, special RAI considerations for healthcare, and the experts she looks to as she continues in this journey.
Speaker: Tempest Van Schaik, PhD, Senior Machine Learning Biomedical Engineer, Microsoft CSE