We invite submissions of 4-page abstracts in CVPR format of current or previously published work addressing the topics outlined below. (For the previously published works re-formatting is not necessary.) We encourage interdisciplinary work, position papers, surveys and other discussions addressing issues that we should consider while conducting and publishing computer vision research.
We will make the accepted submissions available on our website as non-archival reports (there will be no proceedings). The accepted works will be presented at the poster session and some will be selected for oral presentation.
Please, submit your work on Easychair: https://easychair.org/conferences/?conf=fatecv2019
- Techniques and models for fairness-aware visual data mining, information retrieval, recommendation, etc.
- Formalizations of fairness, bias, discrimination; trade-offs and relationships between them.
- Defining, measuring and mitigating problematic biases in datasets and models, improvement of data collection processes to be more fair, diverse, and inclusive.
- Translation of legal, social, and philosophical models of fairness into mathematical objectives.
- Qualitative, quantitative, and experimental studies on perceptions of algorithmic bias and unfairness.
- Measurement and data collection regarding potential unfairness in systems.
- Understanding how tools from causal inference can help us to better reason about fairness and the interplay between prediction and intervention.
- Processes and strategies for developing accountable computer vision systems.
- Methods and tools for ensuring that algorithms comply with fairness policies.
- Metrics for measuring unfairness and bias in different contexts.
- Techniques for guaranteeing accountability without necessitating transparency.
- Techniques for ethical A/B testing.
- Interpretability of computer vision models.
- Generation of explanations for models and algorithmic outputs.
- Design strategies for communicating the logic behind algorithmic systems.
- Trade-offs between privacy and transparency in computer vision systems.
- Qualitative, quantitative, and experimental studies on the effectiveness of algorithm transparency techniques in promoting goals of fairness and accountability.
- Tools and methodologies for conducting audits of computer vision models.
- Empirical results from algorithm audits.
- Frameworks for conducting ethical and legal algorithm audits.
- Analysis of ethical dilemmas presented by recent computer vision works and applications.
- Exclusion and inclusion (e.g., exclusion of certain groups or beliefs, how/when to include stakeholders and representatives for the user population to be served).
- Overgeneralization, undergeneralization, and the cost of different errors (e.g., making false classifications on tasks including facial analysis technologies).
- Exposure (e.g., underrepresentation/overrepresentation of population groups).
- Dual use (e.g., the positive and negative aspects of computer vision applications, the close relationship between government and industry interests and computer vision research).
- Privacy protection (e.g., anonymization of biomedical images, best practices for researchers in industry to ensure the privacy of their users’ data, educating the public about how much industry and government may know about them, privacy protection for data annotated with intrinsic features such as emotion).
- Paper submission deadline: April 19, 2019
- Notification to authors: April 30, 2019
- "Camera ready" deadline (uploading PDFs on our website, non-archival): May 27, 2019
- Should the submission be anonymized?
Answer: no, submissions do not need to be anonymized.
- Does the page limit include references?
Answer: In case you plan to submit the same work to another conference, (to be safe) it should be 4 pages including references.