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Artificial intelligence algorithms have been used to enhance a wide variety of products and services, 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 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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Debugging Database Queries: A Survey of Tools, Techniques, and Users
Database management systems (or DBMSs) have been around for decades, and yet are still difficult to use, particularly when trying to identify and fix errors in user programs (or queries). We seek to understand what methods have been proposed to help people debug database queries, and whether these techniques have ultimately been adopted by DBMSs (and users). We conducted an interdisciplinary review of 112 papers and tools from the database, visualisation and HCI communities. To better understand whether academic and industry approaches are meeting the needs of users, we interviewed 20 database users (and some designers), and found surprising results. In particular, there seems to be a wide gulf between users' debugging strategies and the functionality implemented in existing DBMSs, as well as proposed in the literature. In response, we propose new design guidelines to help system designers to build features that more closely match users debugging strategies.
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- Award ID(s):
- 1850115
- PAR ID:
- 10301348
- Date Published:
- Journal Name:
- CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 16
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3313831.3376485
