Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This blog post covers "On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections" and does a good job of explaining the basic problem with respect to fair machine learning and learning graph embeddings. The illustration and motivating example with inter and intra links are also well presented. However, this blog post could be strengthened if the author could address the following points:

- The blog post could describe better in words what Sufficiency as a dyadic fairness metric seeks to accomplish.
- The presentation of fairness is overtly focused on gender. The blog post could be strengthened by shedding light on other important examples where fairness in Graph ML is a prominent consideration.
- Proposition 4.1 is not well explained. Some of the notation is unclear such as $U$, what is the expectation over?
- Notation in the theorem is again not well explained. If you are going to put a complex theorem it is critical the readers can parse it adequately otherwise mollify/simplify the technical presentation.
- There is too much-bolded text in this blog post which is absolutely not needed. If everything is bolded then the main point---i.e. drawing attention to important concepts---is muddled.
- "Even an iteration of reinforcement learning can be reframed as applying a graph filter to a graph containing nodes representing states and actions!" Not sure what the authors mean by this. This sounds interesting but is not explained enough for it be understood.
- "I believe we should move away from the terminology of “fairness-utility tradeoff” ... “well-performing” model" This a very subjective point of view that could never work in a paper but is very interesting in a blog post. I wish this perspective was expanded on further, perhaps with a bit more technical discussion.

review_title: Blog Post Review for "On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections"

final_score: rating: 7: Good paper, accept
review_content: The blog discusses the paper 'On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections.' The paper studies the fairness issues in graph neural networks and recommendations. The paper relates the unfairness in link predictions to the bias graph connections and proposes an algorithm to learn a fair graph connection. The experimental results enjoy a favorable tradeoff between fairness and accuracy.

Pros:
1. The blog fully explains the basic problem setting and why we need to model the bias in graph data and recommendation systems. A vivid figure greatly helps readers understand this problem. The illustrations explain why the vanilla link prediction can be problematic and how it can bring harm to social network users, which makes the motivation clear and easy to understand.
2. The blog illustrates the fairness notions and gives the concepts of 'inter-link' and 'intra-link.' The figure also helps for understanding. The blog explains the theorem in the paper with sufficient intuitive interpretation.
3. The blog discusses many scenarios beyond the scope of this paper, which can motivate further works.

Cons:
1. The theorems are better presented in text rather than in figures. I would suggest the authors reformat the theorem to make the text consistent throughout the blog.
2. The meaning of some notations in the theorems is missing. I understand that for blog readers, it is unnecessary to understand every variable in a technical theorem, but I would suggest blog authors fully explain every variable before stating the theorem.
3. An illustrative figure for the theorem can be added for better understanding. It can show the bias and unbias graph connections.

Minor:
One minor issue is, under the heading: Formalizations of Dyadic Fairness, the blog authors write 'Suppose we have a directed, homogeneous graph,' while the paper assumes an undirected graph. 
review_title: Reveiw for the blog post 'On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections'

Decision: Accept