Samenwerken op ethisch verantwoord datagebruik: uitdagingen en oplossingen voor betere publieke dienstverlening 1-2 - IBDS

Discussion of how algorithmic affordances in educational recommender systems can help create responsible AI, but how they also potentially introduce new ethical problems

This paper presents a case study about the responsible design of a recommender system of a prominent Dutch Public Service Media (PSM) organization, combining personalized content recommendations to users while aligning with the organization’s overarching mission of fostering diversity. A conceptual framework …

Algorithmic affordances are defined as user interaction mechanisms that allow users tangible control over AI algorithms, such as recommender systems. Designing such algorithmic affordances, including assessing their impact, is not straightforward and practitioners state that they lack resources to design …

This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples …

In flexible education, recommender systems that support course selection, are considered a viable means to help students in making informed course selections, especially where curricula offer greater flexibility. However, these recommender systems present both potential benefits and looming risks, such …

Recommender systems are widely used in today’s society, but many of them do not meet users’ needs and therefore fail to reach their full potential. Without careful consideration, such systems can interfere with the natural decision-making process, resulting in the disregard for recommendations provided. …

Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on determining whether a RecSys is discriminating or not but does not compute the amount …

Recommenders play a significant role in our daily lives, making decisions for users on a regular basis. Their widespread adoption necessitates a thorough examination of how users interact with recommenders and the algorithms that drive them. An important form of interaction in these systems are algorithmic …

Social networks and news outlets use recommender systems to distribute information and suggest news to their users. These algorithms are an attractive solution to deal with the massive amount of content on the web [6]. However, some organisations prioritise retention and maximisation of the number of …