The past few years have brought much hand wringing and arm waving about artificial intelligence (AI), as business people and technologists alike worry about the outsize decisioning power they believe these systems to have. As a data scientist, I am accustomed to being the voice of reason about the possibilities and limitations of AI. In this article I'll explain how companies can use blockchain technology for model development governance, a breakthrough to better understand AI, make the model development process auditable, and identify and assign accountability for AI decisioning. While there is widespread awareness about the need to govern AI, the discussion about how to do so is often nebulous, such as in "How to Build Accountability into Your AI" in Harvard Business Review: A healthy ecosystem for managing AI must include governance processes and structures.... Accountability for AI means looking for solid evidence of governance at the organizational level, including clear goals and objectives for the AI system; well-defined roles, responsibilities, and lines of authority; a multidisciplinary workforce capable of managing AI systems; a broad set of stakeholders; and risk-management processes. Additionally, it is vital to look for system-level governance elements, such as documented technical specifications of the particular AI system, compliance, and stakeholder access to system design and operation information.

Derek Snaidauf is a Principal with Deloitte Transactions and Business Analytics LLP. He is a recognized expert in Artificial Intelligence, Trustworthy AI, Cyber, and Cloud, and is a frequent author and speaker on these topics. Derek holds leadership roles in Deloitte's Strategic Growth Offerings, Analytics & Technology, and Regulatory & Legal Support practices. During his 20 year management consulting and professional services career, Derek has helped numerous clients across industries navigate complexity, boost performance, and anticipate change. He supports them end-to-end on strategy, organizational design, process transformation, data science, and technology.

"AI incidents are events or occurrences in real life that caused or had the potential to cause physical, financial, or emotional harm to people, animals or the environment," as the database defines them. They must meet two qualifications to be filed on the database. First, an identifiable intelligent system must be involved. Second, if the prior is true, the systems must have caused harm or harm could have reasonably resulted from its actions. The system is lenient and would rather include an incident than reject a submission, the site says.

There's no question that Artificial Intelligence (AI) is quickly becoming more prevalent in organisations today. As a result of the burgeoning adoption, AI systems are becoming more complex, and the reasoning behind AI decisions more intricate and less transparent. Machine learning (ML) in particular allows AI systems to develop new rule sets that become increasingly complex over time. Why is this an issue? In many circumstances, it probably isn't – if I get into my driverless car, arrive at my destination safe and on time, does it really matter which road it took to get there?