Tag: ML

A Checklist of Ethical Design Challenges for Business AI

In the first part of this two-part blog, we saw that design principles and digital ethics are helpful to build trust in AI. A good question is: In which areas will we find those ethical challenges? A “checklist” would be helpful to identify the most relevant pitfalls. Such a list must be defined for individual […]
388  | 
Guido Wagner

How Digital Ethics Enables Trust in Business AI

Technologies summarized under the label of “Artificial intelligence” (AI) open doors to new possibilities that can enrich the lives of many humans. This is the first part of a two-part blog that shows that digital ethics can be helpful to manage the risks arising from expected changes in the labor market and our society. The […]
455  | 
Guido Wagner

UX for AI: Building Trust as a Design Challenge

How can I trust a digital assistant to perform tasks that are important to me and what could a trustful relationship between a business user and the digital assistant look like in practice?
456  | 
Vladimir Shapiro

Explainable Artificial Intelligence: It’s (Not) All About Language

How to make AI understandable This blog is part of a series on intelligent system design. In our previous blog Explaining System Intelligence we looked at why it’s vital to explain the underlying models and reasoning behind AI algorithms to the user, and outlined what needs to be explained and when. Now we want to take […]
612  | 
Annette Stotz

Explaining System Intelligence

One of the guiding design principles for intelligent systems is to empower end users. If we want people to trust in machines, we need to share information about the underlying models and the reasoning behind the results of algorithms. This matters even more in business applications, where users are held accountable for every decision they make. In the meantime, it's widely accepted that intelligent systems need to come with a certain level of transparency. There's even a new term for it: explainable AI. But that's just the beginning. As designers, we need to ask ourselves how explainable AI ties in with the user interaction. What do we need to think about whenever we explain the results and recommendations that come from built-in intelligence? And how can we make it a seamless experience that feels natural to users?
628  | 
Vladimir Shapiro

5 Challenges to Your Machine Learning Project

Why think of users when it comes to machine learning? After all, machine learning is for bypassing users, isn’t it? No, it isn’t. Let’s walk through five challenges to see where they do matter.
637  | 
Bernard Rummel

Working with Intelligent Systems

Artificial intelligence will change work life dramatically. But how exactly? And what does this mean for your user interface strategy?
919  | 
Bernard Rummel

Why Digital Ethics Matter

AI is both exciting and unsettling. This series of 3 blogs invites you to explore a future coexistence of people and intelligent machines – and pinpoints what we need to do to make sure AI benefits, rather than harms, humanity. The first part discusses the importance of "Digital Ethics".
1847  | 
Guido Wagner

Human in Control or Automate Everything?

There’s a common misconception that artificial intelligence inevitably means 100% automation. Movie producers would have us believe that AI is going to control absolutely everything – a Skynet scenario à la Terminator. So, if you design and implement an AI system, should you fear this or just ignore it? Is there a roadmap for intelligent automation of a specific system?
814  | 
Vladimir Shapiro

Principles of Intelligent System Design

A properly designed intelligent SAP system extends the cognitive capabilities of a human user. As with past generations of tools, our aim should be to empower users and improve the outcome of human work. Based on our experience in recent projects, we have elaborated several design principles which we would like to share with you.
1438  | 
Vladimir Shapiro