Operationalizing responsible AI
Who is this presentation for?
- Architects, tech leads, data architects, data scientists, and senior developers
In a system that promotes responsibility, designers and architects often face two important questions: how to design mathematical and statistical models using concrete goals for fairness and inclusion and how to architect the system that facilitates it.
Devangana Khokhar and Vanya Seth highlight the latter to help you learn the paradigm of evolutionary architectures to build and operationalize responsible architectures. They outline the aspects you need to keep in mind while architecting responsible-first systems.
The systems engineering realm often talks in terms of “-ilities” important for the designed system. These are also referred to as “nonfunctional requirements,” which should be tracked and accounted for at each step in the process. They serve as guard rails for making sensible decisions. When it comes to architecting fair, accountable, and transparent (FAT) AI systems, Devangana and Vanya propose “responsibility” as one of the guard rails.
You must consider a multitude of inner dimensions when determining whether a system is responsible or not. Some of these are auditability of the data transformation pipeline (how the data is handled and treated at each step, if there’s clear visibility into the expected state of the data, and if logs are present in each of the modules in the data pipeline), monitoring of the crucial metrics (if the logs are available centrally, if there’s clear visibility on the correctness of the pipeline modules, if the data can be interpreted in human-readable form, if you can measure the quality of the data at each step, if there’s an analytical dashboard capable of ingesting the audit logs, if you can to drill down in the dashboard to do root cause analysis, identify anomalies, etc., and if you can create reports about the precision and accuracy of the insights and the intermediate data states), and feedback loops (if you can inject biases and anomalies in the data to test the resilience of the model and the system as a whole, how to engage users in uncovering instances of “responsible” system failure and handle it effectively and efficiently, and how to ensure that the feedback loops aren’t biased).
The most important question to answer is how to operationalize this. Evolutionary architecture talks about a fitness function. If you want the systems to hold this tenet close and always ensure its fulfillment—automate it. Codifying these tenets into tests that give feedback is the right way to think about this. Every time a change is made in the model, the system should provide feedback on its responsibility. The tests should start to fail as the system digresses from the set threshold. There’s often a notion that algorithms are a black box and hard to explain. However, it’s important to acknowledge that even white-box algorithms need explanation and need to be accountable.
- Experience with software architectures
- Familiarity with the principles of evolutionary architectures (useful but not required)
What you'll learn
- Learn about various concepts of evolutionary architectures that come alive while talking about designing and building responsible systems
- Discover a conceptual framework to operationalize responsible AI while building intelligent systems
Devangana Khokhar is lead data scientist and strategist at ThoughtWorks. She brings 6+ years of experience in building intelligent systems and defining data strategy for clients across multiple domains and geographies. Devangana has a research background in theoretical computer science, information retrieval, and social network analysis, and she’s written a book on network sciences, Gephi Cookbook (Packt Publishing London). Her interests include data privacy and security, the role of data in humanitarian sector, ethics and responsibilities around data, reinforcement learning, and data-driven intelligence in low-resource settings. Devangana frequently consults for and guides nonprofit organizations and social enterprises on the value of data literacy and holds workshops and boot camps on various dimensions of data. She earned her master’s degree in theoretical computer science specializing in social network analysis from PSG College of Technology, Coimbatore, India.
Vanya Seth is a lead architect at ThoughtWorks, where she works with clients from various domains and markets, guiding them on building evolutionary architectures. She’s a passionate technologist with a knack for solving complex problems. She brings 10 years of experience in building cloud native applications designed for scale. In her additional role as the technology principal for the Hyderabad office of ThoughtWorks, she formulates technology strategy for the clients and consults them on various aspects such as scalability, security, etc. She has a strong product background, having worked with product-based companies in the past. She also has extensive experience in working with open source communities, particularly with District Health Information System (DHIS2), a widely used open source health platform used across Africa, Asia, and the Indian subcontinent.
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