Fairness and Transparency in Recommendation: The Users’ Perspective
推薦中的公平性和透明度：用戶的視角
儘管推薦系統是由個性化定義的，但最近的工作表明瞭附加的，超出準確性的目標（如公平性）的重要性。
因爲用戶通常希望他們的推薦是完全個性化的，所以這些新的算法目標必須在瞭解公平的推薦系統中透明地傳達。
儘管解釋在推薦系統研究中具有悠久的歷史，但很少有嘗試解釋使用公平性目標的系統的工作。
即使AI的其他領域先前的工作已經探索了將解釋作爲增加公平性的工具，但這項工作並未集中在推薦上。
在這裏，我們考慮了注重公平性的推薦系統和增強透明度的技術的用戶視角。我們描述了一項探索性訪談研究的結果，該研究調查了用戶對公平性、推薦系統和注重公平性的目標的看法。我們提出了三個特徵 —— 根據參與者的需求 —— 可以提高用戶對公平性意識的推薦系統的理解和信任。
Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness.
Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system.
While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective.
Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation.
Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features – informed by the needs of our participants – that could improve user understanding of and trust in fairness-aware recommender systems.