Progressive Disclosure: Designing for Effective Transparency
Progressive Disclosure: Designing for Effective Transparency
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发表时间：
2018-11
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通讯作者：
Aaron Springer;S. Whittaker
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作者：
Aaron Springer;S. Whittaker
随着我们越来越多地将重要决策委托给智能系统，用户了解算法决策是如何做出的至关重要。之前的工作通常采取以技术为中心的方法来提高透明度。相反，我们探索以用户为中心的经验方法，以更好地理解用户对透明系统的反应。我们在两项研究中评估了用户对全局反馈和增量反馈的反应。在研究1中，用户预期更透明的增量系统会表现更好，但在体验该系统后撤回了这一评估。定性数据表明，这种情况可能会出现，因为增量反馈会分散注意力，并破坏用户对系统操作形成的简单启发法。研究 2 深入探讨了这些影响，表明用户可能会从最初简化的反馈中受益，这些反馈隐藏了潜在的系统错误，并帮助用户构建有关系统操作的工作启发法。我们利用这些发现来激发新的渐进式披露原则，以实现智能系统的透明度。
As we increasingly delegate important decisions to intelligent systems, it is essential that users understand how algorithmic decisions are made. Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. We assess user reactions to global and incremental feedback in two studies. In Study 1, users anticipated that the more transparent incremental system would perform better, but retracted this evaluation after experience with the system. Qualitative data suggest this may arise because incremental feedback is distracting and undermines simple heuristics users form about system operation. Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems.