Design Thinking and Algorithms

Tuesday, October 12, 2021

Dr. Laila Shereen Sakr

Science

These two cultures have been long recognized as dominating our social, cultural, and educational systems.

Humanities

Design

An outcome of a research project at the Royal College of Art on "Design in General Education" was the statement of a belief in a missing "third area" of education (1979).

Cross, Nigel (1982). Designerly ways of knowing. Design Studies, 3(4) pp. 221–227.

Science

Humanities
Design

Phenomenon of study

Primary method

Primary value

The collected experience of material culture, and the collected skill and understanding embodied in the arts of planning, inventing, making and doing.

GESTALT - means "shape" or "form"

Major alternative to structuralism in early 20th century.

What are algorithms?

//divide-and conquer

logarithmic

polynomial
traveling salesman problem

exponential

approximation

randomization//

Demystify

• History of the algorithm predates computation: The term ‘algorithm’ predates the digital computer by over a thousand years, with an etymology traceable to the Islamic scholar al-Khwārizmī. Are contemporary algorithms a necessarily computational phenomenon? How can we understand the explosion of discourse about algorithms in popular culture in the last decade?

• Algorithms as more than computation: What does it mean to study algorithms as myth, narrative, ideology, discourse, or power? In what ways can these approaches contribute back to concepts and questions within computer science, data science, and big data initiatives?

• Algorithms as specifically computational: What kinds of applications and activities are now possible given certain developments in computational infrastructure and theories of computation, such as big data, deep neural networks, distributed computing, or ‘microwork’? What are the social and theoretical implications of these developments?

• Algorithmic bias:  Training data encodes biases; data collection feedback loops (find an area with crime, send police there, arrest more people, more crime stats); subpopulation have different observed trends, minority populations necessarily represent smaller portion of data. How can we measure algorithmic bias, if people are biased at any rate?

• Living with algorithms, quantifying social behavior: Algorithms pervade daily life and we experience their reach and impact almost anywhere, not just while working at a computer. How can we better understand how far-flung domains are being reshaped by algorithms? What are the consequences of big data and the quantified self in the everyday and in civic life?