Today’s society consists of humans living in a complex and interconnected world that is intertwined with a variety of computing, sensing and communicating devices – generating massive amounts of data that is systematically stored. AI systems, which are powered by algorithms that learn from data, are driving how humans interact with each other (e.g., social networks), interact with information (e.g., search engines, personalized news feeds), conduct business (e.g., financial trading, sharing economy platforms) and learn (e.g., educational technology).

On the one hand, the ever-increasing speed of computing devices, connectivity of the Internet, the Internet of things, and the sophistication of algorithms comes with the promise of immense prosperity. On the other hand, these advances have revealed their dark side; algorithms can be discriminatory, reinforce human prejudices, polarize opinions, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be.

Our goal is to consider new questions and address emerging problems via an interdisciplinary effort that leverages the understanding of all aspects of human-data-algorithm interactions and leads to the development of tools – both algorithmic and regulatory – that synthesize knowledge from computer science/data science and the social sciences/humanities. Ultimately, these developments will enable well-informed recommendations for approaches to policy-making and governance that are adapted to this new ecosystem. 

Examples of questions and directions include:

1.  Understand how humans behave in the face of algorithms: Measure how human behavior changes when interacting with and through algorithms, and how these changes affect individual and group outcomes.

2. Quantify/understand how algorithms impact humans: Develop quantitative measures and models of how training data affects algorithmic outputs, and how these algorithms, in turn, impact the options available to, opinions,  and decision-making abilities of humans.

3. Design computational methods that empower society: Develop a theory and practice of regulating algorithms and develop tools that empower citizens and policymakers alike over key issues such as privacy, learning, and decision-making.

4. Devise and derive recommendations: Suggest approaches to policy/lawmaking and governance that are adapted to this new world of ubiquitous algorithmic decision-making.

Activities in this initiative include colloquium talks, focussed brainstorming sessions, workshops, translational tutorials, and field days.
If you would like to participate, please get in touch!