What are your services?
We help you reimagine AI - Work with us

What is AI?
We think of AI as Augmented Intelligence. While understanding the potential problems with AI, we believe in using data, design and computation to make humans and organizations better at their craft

How do you see foresight?
In a dialog we'll examine your organization to discover how the future may impact your planning.

How do you approach Strategy and Ethics? 
Data collection and analysis by AI creates a high potential for invasion of privacy, security risks, bias, discrimination, and the unwanted commercial exploitation of our personal data/activities. Given these risks, a thoughtful strategy for AI that is aligned with the user's values and mission needs to be developed.

Do you offer training and workshops?
Yes. By working hands on with AI prototyping, we help your team develop a stronger understanding of AI as a material to shape

Who is Philip van Allen?
Phil is a long-time innovator, researcher and expert in the design of new technologies. He is a maker, writer, speaker, educator, and expert at the intersection of AI, networks, media, data, and the new ecologies these create. Working in a range of platforms and scales, he discovers new approaches (such as animistic design) and possibilities, bringing them to life in surprising ways. He has experience in startup, corporate, entertainment, education, and research contexts. Phil has co-organized the European Design & AI Symposium for the last three years. He has an extensive network in the worlds of design and AI. For more details, interact with Phil’s AI assistant at commotion.ai.

Who are past clients of Phil's?
Philips, TU Delft, Nestlé, Infiniti, Acura, Razorfish, George P Johnson, Launch Magazine/Yahoo, The Huntington Art Collections, USC Rossier School of Education, Interval Research, The Germs, U2, Yoko Ono

Ethics Further discussion

AI Errors: Machine learning depends heavily on being trained on examples, and if the example data is bad or mislabeled, the system can make serious mistakes such as the infamous "gorilla" problem with Google Photos. For example, Google Photos uses AI to tell you who or what is in your photos. But in a horrible example of racism that comes from naive design, poor hiring practices, bad data, and inadequate testing, the system started identifying black people as gorillas.

Unintended Bias: AI can unintentionally reproduce the biases of the designers, organizations, cultures that are the context for creating the system. AI can help reduce bias, but it can also bake in and scale bias — Machine learning depends heavily on being trained on examples, and if the example data is bad or mislabeled, the system can make serious mistakes. 

Surveillance: AI can be used to track people both online and IRL, and this tracking info must be secured by the organization responsible for the data (e.g. home security/automation vendors). Another area of concern is facial recognition in public areas, which can be used to identify where specific people are without their permission. For example, a company (Clearview.ai) is currently compiling photos from social media for facial recognition, and selling an app to police, governments, and others so they can identify people within seconds of capturing a photo.

Privacy: AI can notice very subtle patterns that can reveal private information. For example, a high school student who shopped at Target started receiving pregnancy related marketing before she had told her parents she was pregnant. This is a classic case of unintended consequences, where the sending of marketing materials both revealed private information and made people feel creepy.

Labor disruption: While AI has the potential to increase satisfaction and decrease the drudgery of work, it also can replace or demotivate existing workers. For example, autonomous freight trucks may put some truck drivers out of work. Workers may also feel like their skills are not being utilized well when they must follow what an AI system instructs them on what to do. For example, some delivery workers who have to follow AI created directions are unhappy they can't use the navigating skills and knowledge that they are proud of. These problems for workers are not limited to physical labor, but can indeed affect knowledge workers and their job satisfaction and performance.