Most Likely to Succeed

2021
Interactive Installation
dimensions variable

Most Likely to Succeed is an interactive installation that gives you the chance to prove your skills in an exciting tactile game requiring focus and dexterity. By manipulating a tilting platform, players are tasked with getting as many balls through a maze as possible before time runs out. But there’s a catch—the amount of playing time each person gets is allocated by an AI system. Before playing the game, you must undergo a virtual assessment designed to test skills, such as: reaction time, ability to follow directions, and creative problem solving. Based on your performance, the AI will make a prediction about your likelihood of success in the real-life game. The more confidence the AI system has in your ability, the more time it will invest in your game. Afterward, players receive a performance report that offers metrics for how to improve your assessment score, as well as how much playing time you would have gotten if the system were optimized to give more time to people who it deems less likely to succeed.

AI is increasingly being used to make decisions that impact our lives. From job applications to university admissions to bank loans and insurance claims, machine learning algorithms are making predictions about who will be the best and worst candidate according to a set of criteria that is often hidden away inside a “black box” model. The goal is to increase efficiency and remove the risk of human bias, but how can we ensure that AI systems themselves, and the decisions based on their predictions, are fair? How should we define ‘fairness’ in the first place?

In games, fairness means that all players have an equal opportunity to achieve success. In fact, games actively use mechanics to mitigate potential inequality—for example, flipping a coin to see who gets to make the first move, or handicaps in golf that allow players of different skill levels to compete with one another. However, in real life, there are many conditions and policies that create unfair circumstances. AI systems are introduced with the intent to reduce bias through impartiality, but they are often coded to maximize efficiency and success rates while mitigating risk. Using an AI model that mimics these common real-life standards and applying it to a game, this artwork highlights the potential gap between ‘impartiality’ and ‘fairness.’ It is also designed to encourage visitors to consider which metrics we value and what policies are created based on the resulting model's calculations. 

Most Likely to Succeed is a collaboration with Alexander Taylor for the BIAS: BUILT THIS WAY exhibition at Science Gallery at Trinity College in Dublin, Ireland. It marks the culmination of a curator in residence program with Julia Kaganskiy at Science Gallery Dublin and Accenture Labs, in Accenture’s flagship R&D and global innovation centre, The Dock, Dublin. It was fabricated by Think Design in Dublin, Ireland.

More Information:

• Video: FAIR IS FAIR... ISN'T IT? - panel about AI and fairness with Science Gallery Dublin and Accenture’s The Dock