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Building Ethical AI

  1. 1. A Guide To Building Ethical AI
  2. 2. Agenda PROBLEM CONTEXT SOLUTION - OPERATIONALIZE AI ETHICS AND DATA DON’TS FOR AI ETHICS RISK MITIGATION APPROACH: HOW TO OPERATIONALIZE DATA AND AI ETHICS
  3. 3. Problem Context While AI scales solutions at the same time it scales risks as well as an example ethics. AI ethics and data are business necessities. With this emerging trend companies need to publish an action plan for facing the ethical uncertainties.
  4. 4. Solution – Operationalize AI ethics and data Identify existing infrastructure that a data and AI ethics program can leverage.Identify Create a data and AI ethical risk framework that is tailored to a given industry.Create Change how ethics is perceived.Change Optimize guidance and tools for product managers.Optimize Build organizational awareness.Build Inspire employees to play a role in identifying AI ethical risks.Inspire Monitor impacts and engage stakeholders.Monitor
  5. 5. Challenges faced if AI ethics is not operationalized? Wasted resources Inefficiencies in product development and deployment Inability to use data for training AI models
  6. 6. Don’ts for AI ethics risk mitigation • Academic approach: results in absence of clear directives to the developers on the ground and the senior leaders who need to identify and choose among a set of risk mitigation strategies. • On-the-ground approach by engineers, PMs and data scientists: Lack of expertise and org support to answer systematically and efficiently any questions. • High-level AI ethics principles: Rolling out principles does not help because there are no clear answers to what is meant by fairness and which metrics is the right one for decision making.
  7. 7. Approach: how to operationalize AI ethics • Frame the problem before developing AI algorithms by engaging with relevant stakeholders early in the development process and articulate what the product does and does not do. • Establish KPIs, ethical standards and a governance structure to measure the continued effectiveness of the tactics carried out for the AI ethics strategy. • Form an ethics council with members of data, security, legal, compliance and external subject matter experts (includes ethicists). Create processes to vet for biased algorithms, privacy violations, and unexplainable outputs. For every AI initiative go through this council for review prior to development and deployment of AI models. • Develop quality assurance programs during product development with: • Self-serve tools for PMs to validate bias and understand explain-ability of algorithms to make informed trade-offs. • Define how people’s data is collected, used, and shared • Break down big ethical concepts like privacy, bias, and explain-ability into infrastructure, process and practice. • Increase organizational awareness with necessary trainings and collaterals. • Reward people for their efforts in promoting a data ethics program is essential. • Monitor the impacts of the data and AI products that are on use.
  8. 8. Reference • https://hbr.org/2020/10/a-practical-guide-to-building-ethical- ai?utm_medium=social&utm_source=instagram&utm_campaign=hav e2haveit

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