In the past decade, machine learning based decision systems have been widely used in a wide range of application domains, like credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the support of these systems has an immense potential to improve the decision in different fields, their use may present ethical and legal risks, such as codifying biases, jeopardizing transparency and privacy, and reducing accountability. Unfortunately, these risks arise in different applications. They are made even more serious and subtly by the opacity of recent decision support systems, which are often complex and their internal logic is usually inaccessible to humans.
Nowadays, most Artificial Intelligence (AI) systems are based on Machine Learning algorithms. The relevance and need for ethics in AI are supported and highlighted by various initiatives arising from the researches to provide recommendations and guidelines in the direction of making AI-based decision systems explainable and compliant with legal and ethical issues. These include the EU's GDPR regulation which introduces, to some extent, a right for all individuals to obtain ``meaningful explanations of the logic involved'' when automated decision making takes place, the ``ACM Statement on Algorithmic Transparency and Accountability'', the Informatics Europe's ``European Recommendations on Machine-Learned Automated Decision Making'' and ``The ethics guidelines for trustworthy AI'' provided by the EU High-Level Expert Group on AI.
The challenge to design and develop trustworthy AI-based decision systems is still open and requires a joint effort across technical, legal, sociological and ethical domains.
The purpose of XKDD, eXaplaining Knowledge Discovery in Data Mining, is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning. Also, this year the workshop will seek submissions addressing uncovered important issues in specific fields related to eXplainable AI (XAI), such as XAI for a more Social and Responsible AI, XAI as a tool to align AI with human values, XAI for Outlier and Anomaly Detection, quantitative and qualitative evaluation of XAI approaches, and XAI case studies. The workshop will seek top-quality submissions related to ethical, fair, explainable and transparent data mining and machine learning approaches. Papers should present research results in any of the topics of interest for the workshop, as well as tools and promising preliminary ideas. XKDD asks for contributions from researchers, academia and industries, working on topics addressing these challenges primarily from a technical point of view but also from a legal, ethical or sociological perspective.
Topics of interest include, but are not limited to:
Electronic submissions will be handled via CMT.
Papers must be written in English and formatted according to the Springer Lecture Notes in Computer Science (LNCS) guidelines following the style of the main conference (format).
The maximum length of either research or position papers is 14 pages references excluded. Overlength papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as overlength).
Authors who submit their work to XKDD 2023 commit themselves to present their paper at the workshop in case of acceptance. XKDD 2023 considers the author list submitted with the paper as final. No additions or deletions to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera ready stage.
Condition for inclusion in the post-proceedings is that at least one of the co-authors has presented the paper at the workshop. Pre-proceedings will be available online before the workshop.
All accepted papers will be published as post-proceedings in LNCSI and included in the series name Lecture Notes in Computer Science.
All papers for XKDD 2023 must be submitted by using the on-line submission system at CMT.
Although ECML PKDD 2023 will guarantee a streaming service in all rooms and satellite events, this year's organization aims to maximize engagement and physical presence in Turin. The provided streaming service, with the associated remote registration fee, is considered an option only for non-presenting attendees. The goal is to avoid having little participation in the rooms, with the events happening almost exclusively "virtually." Therefore, we will adopt the same rules as the main event also for our workshop: at least one author of each accepted paper must have a full registration and be in Turin to present the paper. Papers without a full registration or in-presence presentation won't be included in the post-workshop Springer proceedings.
The event will take place at the ECML-PKDD 2023 Conference at the Officine Grandi Riparazioni (OGR) .
Additional information about the location can be found at
the main conference web page: ECML-PKDD 2023
ECML-PKDD 2023 plans TBC
This workshop is partially supported by the European Community H2020 Program under research and innovation programme, grant agreement 834756 XAI, science and technology for the explanation of ai decision making.
This workshop is partially supported by the European Community H2020 Program under the funding scheme FET Flagship Project Proposal, grant agreement 952026 HumanE-AI-Net.
This workshop is partially supported by the European Community H2020 Program under the funding scheme INFRAIA-2019-1: Research Infrastructures, grant agreement 871042 SoBigData++.
This workshop is partially supported by the European Community H2020 Program under research and innovation programme, grant agreement 952215 TAILOR.
This workshop is partially supported by the European Community H2020 Program under research and innovation programme, SAI. CHIST-ERA-19-XAI-010, by MUR (N. not yet available), FWF (N. I 5205), EPSRC (N. EP/V055712/1), NCN (N. 2020/02/Y/ST6/00064), ETAg (N. SLTAT21096), BNSF (N. KP-06-AOO2/5). SAI.
This workshop is partially supported by the European Community NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research). FAIR.
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