Clarisse Sieckenius de Souza

Rafael Brandão, Joel Carbonera, Clarisse de Souza et al.

The presumed data owners' right to explanations brought about by the General Data Protection Regulation in Europe has shed light on the social challenges of explainable artificial intelligence (XAI). In this paper, we present a case study with Deep Learning (DL) experts from a research and development laboratory focused on the delivery of industrial-strength AI technologies. Our aim was to investigate the social meaning (i.e. meaning to others) that DL experts assign to what they do, given a richly contextualized and familiar domain of application. Using qualitative research techniques to collect and analyze empirical data, our study has shown that participating DL experts did not spontaneously engage into considerations about the social meaning of machine learning models that they build. Moreover, when explicitly stimulated to do so, these experts expressed expectations that, with real-world DL application, there will be available mediators to bridge the gap between technical meanings that drive DL work, and social meanings that AI technology users assign to it. We concluded that current research incentives and values guiding the participants' scientific interests and conduct are at odds with those required to face some of the scientific challenges involved in advancing XAI, and thus responding to the alleged data owners' right to explanations or similar societal demands emerging from current debates. As a concrete contribution to mitigate what seems to be a more general problem, we propose three preliminary XAI Mediation Challenges with the potential to bring together technical and social meanings of DL applications, as well as to foster much needed interdisciplinary collaboration among AI and the Social Sciences researchers.

Juliana Soares Jansen Ferreira, Clarisse Sieckenius de Souza, Rafael Rossi de Mello Brandão et al.

We present SigniFYI-CDN, an inspection method built from previously proposed methods combining Semiotic Engineering and the Cognitive Dimensions of Notations. Compared to its predecessors, SigniFYI-CDN simplifies procedural steps and supports them with more analytic scaffolds. It is especially fit for the study of interaction with technologies where notations are created and used by various people, or by a single person in various, and potentially distant, occasions. In such cases, notations may serve several purposes, like (mutual) comprehension, recall, coordination, negotiation, and documentation. We illustrate SigniFYI-CDN with highlights from the evaluation of a computer tool that supports qualitative data analysis. Our contribution is a simpler tool for researchers and practitioners to probe the power of combined communicability and usability analysis of interaction with increasingly complex data-intensive applications.

Luiz Marques Afonso, João Antonio Marcondes Dutra Bastos, Clarisse Sieckenius de Souza et al.

In addition to their vital role in professional software development, Application Programming Interfaces (APIs) are now increasingly used by non-professional programmers, including end users, scientists and experts from other domains. Therefore, good APIs must meet old and new user requirements. Most of the re-search on API evaluation and design derives from user-centered, cognitive perspectives on human-computer interaction. As an alternative, we present a lower-threshold variant of a previously proposed semiotic API evaluation tool. We illustrate the procedures and power of this variant, called API Signification Inspection (API-SI), with Keras, a Deep Learning API. The illustration also shows how the method can complement and fertilize API usability studies. Additionally, API-SI is packaged as an introductory semiotic tool that API designers and researchers can use to evaluate the communication of design intent and product rationale to other programmers through implicit and explicit signs thereof, encountered in the API structure, behavior and documentation.