The user experience of our daily interactions is increasingly shaped with the aid of AI, mostly as the output of recommendation engines. However, it is less common to present users with possibilities to navigate or adapt such output. In this paper we argue that adding such algorithmic controls can be a potent strategy to create explainable AI and to aid users in building adequate mental models of the system. We describe our efforts to create a pattern library for algorithmic controls: the algorithmic affordances pattern library. The library can aid in bridging research efforts to explore and evaluate algorithmic controls and emerging practices in commercial applications, therewith scaffolding a more evidence-based adoption of algorithmic controls in industry. A first version of the library suggested four distinct categories of algorithmic controls: feeding the algorithm, tuning algorithmic parameters, activating recommendation contexts, and navigating the recommendation space. In this paper we discuss these and reflect on how each of them could aid explainability. Based on this reflection, we unfold a sketch for a future research agenda. The paper also serves as an open invitation to the XAI community to strengthen our approach with things we missed so far.
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accepted abstract Quis14 conference Field findings show that value dimensions in legal services are functional, social and emotional. The last category emerges not only within but also outside the interaction with the lawyer. Recommendation of others or the trackrecord of lawyers for example, which play a role before or after the service, contribute to emotional values like trust and reassurance and help clients to reduce the perceived purchase risk, which is inherent to the nature of credence services. Also due to the credential character of legal services we conclude that not only professional skills but also service aspects as client involvement play an important role in the emergence of value because professional skills are difficult to judge even by routine buyers.
The research proposal aims to improve the design and verification process for coastal protection works. With global sea levels rising, the Netherlands, in particular, faces the challenge of protecting its coastline from potential flooding. Four strategies for coastal protection are recognized: protection-closed (dikes, dams, dunes), protection-open (storm surge barriers), advancing the coastline (beach suppletion, reclamation), and accommodation through "living with water" concepts. The construction process of coastal protection works involves collaboration between the client and contractors. Different roles, such as project management, project control, stakeholder management, technical management, and contract management, work together to ensure the project's success. The design and verification process is crucial in coastal protection projects. The contract may include functional requirements or detailed design specifications. Design drawings with tolerances are created before construction begins. During construction and final verification, the design is measured using survey data. The accuracy of the measurement techniques used can impact the construction process and may lead to contractual issues if not properly planned. The problem addressed in the research proposal is the lack of a comprehensive and consistent process for defining and verifying design specifications in coastal protection projects. Existing documents focus on specific aspects of the process but do not provide a holistic approach. The research aims to improve the definition and verification of design specifications through a systematic review of contractual parameters and survey methods. It seeks to reduce potential claims, improve safety, enhance the competitiveness of maritime construction companies, and decrease time spent on contractual discussions. The research will have several outcomes, including a body of knowledge describing existing and best practices, a set of best practices and recommendations for verifying specific design parameters, and supporting documents such as algorithms for verification.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Hogeschool Rotterdam wil in samenwerking met IT-Campus en Rotterdamse mkb-bedrijven onderzoeken of de dataskills die studenten in hun opleiding verwerven, aansluiten op de datageletterdheid die van hen als startende professionals wordt verlangd. Om dit te beoordelen vragen we Rotterdamse ondernemers naar de datagedreven uitdagingen en problemen die zij voor zich zien en of zij bij de instroom van startende professionals voldoende kennis en skills zien om die uitdagingen het hoofd te bieden. Met de uitkomsten kunnen kennisinstellingen een helder beeld krijgen van het concept datageletterdheid en hiermee een handvat bieden aan opleidingen om dataskills in de curricula aan te laten sluiten op de behoefte in de arbeidsmarkt van de Metropoolregio Rotterdam-Den Haag (MRDH). We werken toe naar een ontwerp Data Skills-set. Misschien is het beter om te spreken van datacompetenties, hetgeen onderdeel is van de zoektocht in dit onderzoek. Welke terminologie is het meest behulpzaam in het oplijnen van onderwijs en werkveld op het gebied van data: geletterdheid, competenties, skills of een combinatie daarvan. Is het van belang of juist contraproductief om daarin (merk)specifieke tooling een plek te geven? We vragen ons ook af of datageletterdheid als een generiek concept domeinoverstijgend bruikbaar is, bijvoorbeeld tussen het economisch en technisch domein. De verwachting is dat de bevindingen op het gebied van datageletterdheid in de regio Rotterdam te generaliseren zijn naar andere delen van Nederland. Ook die hypothese willen we verkennen in dit onderzoek. Door het beantwoorden van deze vragen willen we een start maken voor het ontwerp van een instrument voor professionele ontwikkeling in het werkveld als ook een referentiekader voor het gesprek met onderwijspartners en overheid. Daarnaast kan zo’n ontwerp DataSkills-set ervoor zorgen dat de onderwijsdomeinen in gesprek blijven met elkaar ten aanzien van nieuwe methoden en onderwijsvormen voor vaardigheden.
