Responsible AI ★★★ Expert Level
Learn how to sharpen the vision on responsible AI within your business. Adopt a broad view on AI and assess the topic from both a data science, as well as a business and leadership lens.
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About the courseAdoption of Artificial Intelligence (AI) has tripled in 12 months and it creates great opportunities. But this rise of AI also kicks starts debates about the concerns and the risks of AI. Discriminating algorithms, privacy violations and unexplainable model outcomes are just a small selection of today’s news bulletins on this topic. In this course, you will learn how you can use Artificial Intelligence responsibly within your organization. This one-day course starts by highlighting the increased need of Responsible AI. To realize this in your organization, the Responsible AI framework is used, consisting of six key pillars: do no harm, fairness, reliability, protect privacy, transparency, and human control. These six pillars are explained in detail with extensive use of real-life business examples. Significant time is spent on discussing different definitions of model fairness and equipping you with the skills to assess model fairness and correcting for it in AI applications. Finally, the organizational enablers of the Responsible AI framework are covered. During the various case studies, participants are challenged to lead the debate on this topic and to approach cases from different angles and hereby challenge own bias. Ethical considerations are hardly ever clear-cut answers. Also in this training, there are always different perspectives and tradeoffs when it comes to Responsible AI. We therefore aim to incite fiery and productive discussions during this training, where you are encouraged to form critical thought on applying AI responsibly and asking the right questions within your organization.
Why this is for youResponsible AI (also called ‘ethical AI’) is not only the concern of philosophers but of all who use AI, whether it is in their organization or in their personal life. Over the past years, and especially since the introduction of the GDPR, there is a growing awareness of using Data and AI responsibly. There are many examples of companies that have incurred PR damage due to irresponsible or discriminating AI applications, often unintentionally. Hence, when implementing AI in your business, it is key to first understand the impact irresponsible AI applications can have on your customers, your employees or your company’s PR image. Only by employing AI in a responsible way you can capture its full potential. As becomes clear in this course, realizing Responsible AI is not a one (wo)man job, it should be embraced and pursued by the whole organization. Knowing what your role and responsibility is, is key to contribute to these ambitions. After this course, you will confidently be able to identify the risks of AI within your job and business as a whole and be equipped with the tools to avoid these risks in the development of models and the use of AI business applications.
For whomThis course is perfect for everyone looking to sharpen their vision on responsible AI within their business. We adopt a broad view on AI and assess the topic from both a data science, as well as a business and leadership lens. Therefore, this course applies perfectly to non-technical groups with no previous modeling skills.
What you’ll learn
- The opportunities and risks of AI and its challenges for organizations
- An integrated framework of what Responsible AI entails
- Applying the Responsible AI pillars on real-life cases and leading the debate
- Methods to assess and improve model fairness
- Organizational and technological enablers of Responsible AI
- Need for Responsible AI - Being able to explain why Responsible AI is not only the concern of ethics philosophers, but also AI business practitioners and AI experts
- Pillars of Responsible AI - Understanding the underlying pillars of Responsible AI and learning how to use them to challenge AI solutions
- Creating Responsible Algorithms - Putting the pillars to practice to ensure AI solutions are responsible