Artificial intelligence algorithms have been applied to a wide variety of tasks , including assisting human decision making in high-stake contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for domain stakeholders to understand algorithms and deploy them in a way that respects their goalsand values. We created an interactive interface to help stakeholders explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their values . We evaluated our interface on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. The interface proved effective at communicating algorithm trade-offs and supporting human decision making and also affected participants' trust in algorithmically aided decision making. Our results have implications for the deployment of intelligent algorithms and suggest important directions for future work .
Artificial intelligence algorithms have been used to enhance a wide variety of products and services , including assisting human decision making in high-stakes contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for designers to understand algorithms and design products or services in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their goals and needs . We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating .
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applied to
used to enhance
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tasks
products and services
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high-stake
high-stakes
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domain stakeholders
designers
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deploy them
design products or services
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their goalsand values. We created an interactive interface to help stakeholders
users' goals, values, and needs. We proposed a method to help designers and users
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values
goals and needs
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interface
method
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The interface proved effective at communicating algorithm trade-offs and supporting human decision making and also affected participants' trust in algorithmically aided decision making. Our results have implications for the deployment of intelligent algorithms and suggest important directions for future work
Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating