Call for Papers: Special issue on Bias and Fairness in AI
Guest Editors: T. Calders, E. Ntoutsi, M. Pechenizkiy, B. Rosenhahn, S. Ruggieri
Data Mining, Machine Learning, and Artificial Intelligence techniques are increasingly used to guide decisions with significant impact in people’s lives, including hiring decisions, university admissions, loan granting, medical diagnosis, and crime prediction. These techniques are applied by search engines, Internet recommendation systems and social media bots, influencing our perception of political developments and even of scientific findings. However, there are growing concerns about the epistemic and normative quality of automated decision making. There is strong evidence that algorithms may sometimes amplify rather than eliminate existing bias and discrimination, and thereby have negative effects on social cohesion and democratic institutions.
The domain is receiving a growing amount of attention in the research community and in the public opinion. Scholarly reflection of these issues is ongoing and several top conferences regularly receive numerous submissions on the topic and the number and breadth of papers in the field is constantly growing. Despite the large volume of related research lately, many challenging problems are still open. We lack a comprehensive understanding of how pertinent concepts of bias or discrimination should be interpreted in the context of AI and which technical options to combat bias and discrimination are both realistically possible and normatively justified.
The special issue aims to bring together a comprehensive overview of current state-of-the-art techniques in the broad domain of ethical aspects of data mining and machine learning. We believe such an overview is timely and necessary to identify and shape future research directions for AI based on legal and ethical grounds. The topics of interest include:
- Formalization, measurement and mitigation of bias and unfairness in machine learning
- New or reconciled fairness impossibility results
- Fairness, equity and justice by design
- Bias and fairness in supervised learning, unsupervised learning and reinforcement learning
- Bias and fairness in non-iid data including network, text, time series and other complex evolving data
- Bias and fairness studies for specific domains, including: federated learning, matchmaking, recommenders and search engines, resource allocation
- Bias and fairness in personalized interventions
- Causal and counterfactual reasoning for bias and fairness
- Formal verification approaches for fairness
- Auditing AI-based system with respect to bias and fairness
- Algorithmic debiasing strategies by pre-, in-, or post-processing techniques
- “Interaction” between fairness and other learning challenges like imbalanced data, rare classes and evolving data
- Explainability, traceability, data and model lineage
- Efficiency and scalability issues of debiasing and fair methods for big data
- Case studies of bias and fairness-aware data mining
All manuscripts have to be prepared according to the DAMI guidelines and submitted through DAMI journal editorial manager. Authors are requested to carefully read the instructions for authors www.springer.com/10618 before submitting their manuscripts.
The steps to select the special issue are as follows: select “Manuscript” as the paper type in the first step, and in step 4 (Additional information) answer the question “Does this manuscript belong to a special issue?” affirmatively and select “S.I. Bias and Fairness in AI” as the special issue. Each submission will be reviewed according to rigorous standards of DAMI. This implies that extensions of conference papers are allowed as long as they differ from the conference versions in that they provide a mature and in a way final view on the investigated subject, without the length restrictions etc. that a conference publication implies. There is no strict rule on how much new material a paper should contain, but it must contain enough new material to provide a significant contribution beyond the conference paper.
Submission deadline: August 31, 2021
Reviewing round 1 ends: October 2021
Revisions: November 2021
Final Acceptance: December 2021
The papers will go through the normal processing pipeline for the journal, including online publication of accepted manuscripts ahead of the special issue being published.
The processing of the submitted papers will start as soon as they are received by the guest editors.
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