Conference on Fairness, Accountability, and Transparency (FAT* 2019)

FAT* is an international and interdisciplinary peer-reviewed conference that seeks to publish and present work examining the fairness, accountability, and transparency of algorithmic systems.

### Topics of Interest ###

The FAT* conference solicits work from a wide variety of disciplines, including computer science, statistics, the humanities, and law. FAT* welcomes submissions that touch on any of the following topics (broadly construed):

# Fairness
- Techniques and models for fairness-aware data mining, information retrieval, recommendation, etc.
- Formalizations of fairness, bias, discrimination; trade-offs and relationships between them
- Defining, measuring and mitigating biases in data sets; improving data collection processes; combining different sources of information
- Translation of legal, social, and philosophical models of fairness into mathematical objectives
- Qualitative, quantitative, and experimental studies on perceptions of algorithmic bias and unfairness
- Design interventions to mitigate biases in systems, or discourage biased behavior from users
- 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
- Analyses of the impact of algorithmic experimentation and exploration

# Accountability
- Processes and strategies for developing accountable 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 autonomous and A/B testing

# Transparency
- Interpretability of machine learning models
- Generation of explanations for algorithmic outputs
- Design strategies for communicating the logic behind algorithmic systems
- Trade-offs between privacy and transparency
- Qualitative, quantitative, and experimental studies on the effectiveness of algorithm transparency techniques in promoting goals of fairness and accountability
- Tools and methodologies for conducting algorithm audits
- Empirical results from algorithm audits
- Frameworks for conducting ethical and legal algorithm audits

This list of topics is not meant to be all-inclusive. Authors who are unclear about whether their work falls within the purview of the FAT* conference should contact the PC Chairs for clarification. At least one author of each accepted paper is required to register for, attend, and present the work at the conference in order for the paper to appear in the conference proceedings in the ACM Digital Library.

### Tracks ###

To ensure that all submissions to FAT* are reviewed by a knowledgeable and appropriate set of reviewers, the conference is divided into tracks. Authors must choose one or more of the following tracks when they register their submissions:

1. Theory and Security
2. Statistics, Machine Learning, Data Mining
3. Applications (NLP, Computer Vision, Search Engines, and other Systems)
4. Systems (Programming Languages, Databases)
5. Human-Computer Interaction (HCI) and Information Visualization
6. Measurement and Algorithm Audits
7. Empirical Studies (Qualitative, Quantitative, Experimental, Etc.)
8. Law, Policy, and Humanistic/Critical Analysis

Each track will have several chairs who will oversee the corresponding papers.

### Archival and Non-archival ###

FAT* 2019 offers authors the choice of archival and non-archival paper submissions. Archival papers will appear in the published proceedings of the conference, if they are accepted; conversely, accepted non-archival papers will only appear as abstracts in the proceedings. FAT* offers a non-archival option to avoid precluding the future submission of these papers to area-specific journals. Note that all submissions will be judged by the same quality standards, regardless of whether the authors choose the archival or non-archival option. Furthermore, reviewers will not be told whether submissions under review are archival or not, to avoid influencing their evaluations.

Authors of all accepted papers must present their work at the FAT* 2019 conference, regardless of whether their paper is archival or non-archival.

### Submission Format and Guidelines ###

# Submission Site

FAT* 2019 is using the submission website is TBD. It will be announced at https://fatconference.org/2019/cfp.html .

Authors are required to pre-register their papers through the submission site by submitting a tentative title and abstract and specifying their submission track(s) by the pre-registration deadline. This process will enable the program chairs to better anticipate the submission load and to make necessary adjustments to the program committee. Those who do not pre-register their submission by the stated deadline will be unable to submit their paper to FAT*. Authors will be able to make changes to their titles and abstracts up until the full paper submission deadline.

# Length and Formatting

Submitted papers must be 8-10 pages (including all figures, tables, and references), plus unlimited pages for appendices. Reviewers will not be required to read material in appendices, so authors are encouraged to use them judiciously.

Papers should be formatted using the 2017 ACM Master Article Template. For LaTeX users, choose format=sigconf. This is the typical, two-column proceedings-style template. Authors do not need to include terms, keywords, or other front matter in their submissions. ACM also makes a Word template available; however, authors who wish to eschew the ACM Word template may submit manuscripts in two-column format, with one inch margins, 9 point Times New Roman font.

# Topic and Archival Selection

Authors will select (1) one or more topics for their submission and (2) whether their submission is archival or non-archival during paper registration. The selected topic(s) will determine the pool of PC members who will review the submission. The PC Chairs reserve the right to move submissions between topics if the PC feels that a submission has been misclassified.

# Double Blind Reviewing

FAT* uses a double blind review process. Authors must omit their names and affiliations from submissions, and avoid obvious identifying statements. Citations to the authors' own prior work should be made in the third-person. Submissions that do not comply with this policy will be rejected without review.

Confidentiality of submitted material will be maintained. Upon acceptance, the titles, authorship, and abstracts of papers will be released prior to the conference.

# Extensions of Workshop Papers

FAT* welcomes submission of work that has previously appeared in non-archival venues. These works may be submitted as-is or in an extended form. FAT* also welcomes full paper submissions that extend previously published short papers (e.g. from workshops). Authors must still take care to comply with the double blind reviewing requirements when submitting extensions of prior work.

# Representations

Submitting authors make the following representations about their work (drawn from the ACM Author Policy Representations):

- That the paper submitted is original, that the listed authors are the creators of the work, that each author is aware of the submission and that they are listed as an author, and that the paper is an honest representation of the underlying work.
- (For papers submitted as archival) that the paper is not currently under review at any other publication venue, and that it will not be submitted to another venue unless it has been rejected or withdrawn from this venue.
- An author of the paper must register for and attend the conference to present the paper, should it be accepted.

Violation of these policies may results in rejection of the submission.

### Ethics ###

Papers that (1) describe experiments with users and/or deployed systems (e.g., websites or apps), or that (2) rely on sensitive user data (e.g., social network information), must follow basic precepts of ethical research and subscribe to community norms. These include: respect for privacy, secure storage of sensitive data, voluntary and informed consent if users are placed at risk, avoiding deceptive practices when not essential, beneficence (maximizing the benefits to an individual or to society while minimizing harm to the individual), risk mitigation, and post-hoc disclosure of audits. When appropriate, authors are encouraged to include a subsection describing these issues. Authors may want to consult the Menlo Report for further information on ethical principles, the Allman/Paxson IMC'07 paper for guidance on ethical data sharing, and the Sandvig et al. '14 paper on the ethics of algorithm audits.

Note that submitting research for approval by each author’s institutional ethics review body (IRB) may be necessary in some cases, but by itself may not be sufficient. In cases where the PC has concerns about the ethics of the work in a submission, the PC will consider the ethical soundness and justification of the submission, just as it does its technical soundness. The PC takes a broad view of what constitutes an ethical concern, and authors agree to be available at any time during the review process to rapidly respond to queries from the PC Chairs regarding ethical considerations. Authors unsure about topical fit or ethical issues are welcome to contact the PC Chairs.