Privacy, Trust and Fairness in Data
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (22 April 2022) | Viewed by 21487
Special Issue Editors
Interests: data quality; uncertainty in data; probabilistic databases; information extraction; data integration
Interests: compliance checking; process mining; information auditing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The application of artificial intelligence in business, healthcare, engineering, education, and many more domains holds much potential for improved quality and efficiency. However, there are threats that endanger this potential: information misuse and algorithmic irregularities. Proper assurances and solutions are needed for society to trust and further depend on this technology in all such application domains. Data analytics and articificial intelligence designed with privacy-preservation, trust-building, and fair data usage can maximize potential while minimizing risks.
The Special Issue on on ‘Privacy, Trust, and Fairness in Data’ of Applied Sciences (ISSN 2076-3417) aims to collect research contributions from a wide range of disciplines and domains directly or indirectly related to privacy, trust, and fairness aspects of artificial intelligence. We invite contributions ranging from theoretical or conceptual papers to technical algorithmic ones as well as applications and case studies. Topics include, but are not limited to:
- Trustworthy artificial intelligence and machine learning;
- Foundations and models for privacy, trust, and fairness;
- Algorithms for privacy, trust, and fairness;
- Application of machine learning for privacy, trust, and fairness;
- Social Influences on privacy, trust, and fairness;
- Impact of issues with privacy, trust, and fairness;
- Quality assurance of privacy, trust, and fairness;
- Ethics of privacy, trust, and fairness;
- Case studies in privacy, trust, and fairness;
- Perception of privacy and trust;
- Privacy preservation;
- Privacy-utility trade-off;
- Resiliency and robustness of algorithms against data quality and fairness issues;
- Information and data quality measurement, curation, and assurance;
- Bias, fairness, and integrity of algorithms;
- Transparency, accountability, and explainability of algorithms and data processing;
- Fairness and integrity in data utilization and organizational goals.
Dr. Maurice Van Keulen
Dr. Faiza Allah Bukhsh
Prof. Dr. Christin Seifert
Guest Editors
Manuscript Submission Information
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Keywords
- privacy
- data utility
- trust
- data quality
- fairness
- bias
- transparency
- explainability
- trustworthy AI
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