Peter Polack introduces his paper "Beyond algorithmic reformism: Forward engineering the designs of algorithmic systems" in Big Data & Society 7(1), https://doi.org/10.1177/2053951720913064. First published: March 20, 2020.
Video abstract
Text abstract
This article develops a method for investigating the consequences of algorithmic systems according to the documents that specify their design constrains. As opposed to reverse engineering algorithms to identify how their logic operates, the article proposes to design or "forward engineer" algorithmic systems in order to theorize how their consequences are informed by design constraints: the specific problems, use cases, and presuppositions that they respond to. This demands a departure from algorithmic reformism, which responds to concerns about the consequences of algorithmic systems by proposing to make algorithms more transparent or less biased. Instead, by investigating algorithmic systems according to documents that specify their design constraints, we identify how the consequences of algorithms are presupposed by the problems that they propose to solve, the types of solutions that they enlist to solve these problems, and the systems of authority that these solutions depend on. To accomplish this, this article develops a methodological framework for researching the process of designing algorithmic systems. In doing so, it proposes to move beyond reforming the technical implementation details of algorithms in order to address the design problems and constraints that underlie them.
Keywords: Critical algorithm studies, predictive policing, design studies, algorithmic bias, algorithmic opacity, algorithmic accountability
Showing posts with label bias. Show all posts
Showing posts with label bias. Show all posts
Wednesday, 25 March 2020
Saturday, 7 March 2020
How biased is the sample? Reverse engineering the ranking algorithm of Facebook’s Graph application programming interface
Justin Chun-ting Ho
Big Data & Society 7(1), https://doi.org/10.1177/2053951720905874. First published February 17, 2020.
Keywords: Bias detection, data mining, Facebook pages, application programming interface, social media research
Since November 2017, Facebook has introduced a new limitation on the maximum amount of page posts retrievable through their Graph application programming interface (API). However, there is limited documentation on how these posts are selected (Facebook 2017). In this article, I assess the bias caused by the new limitation by comparing two datasets of the same Facebook page, a full dataset obtained before the introduction of the limitation and a partial dataset obtained after. To establish generalisability, I also replicate the findings with data from another Facebook page.
This paper demonstrates that posts with high user engagement, Photo posts and Video posts, are over-represented while Link posts are under-represented. Top-term analysis reveals that there are significant differences in the most prominent terms between the full and partial dataset. This paper also reverse-engineered the new application programming interface’s ranking algorithm to identify the features of a post that would affect its odds of being selected by the new API. The estimated model posits that post types, Likes, Angry, Shares, and Likes on Comment are significant predictors. Sentiment analysis reveals that there are significant differences in the sentiment word usage between the selected and non-selected posts.
These findings have significant implications for research that use Facebook page data collected after the introduction of the limitation:
- The under-representation of Link posts means that a significant amount of link-sharing activities would become invisible from the API.
- There is no evidence to support the common expectation that the API would rank posts based on the amount of Likes and Comments. While the selected posts seem to have more Likes and Comments, other features also have an effect on the odds of being selected.
- It is questionable to assume that the new API would return all the posts with the highest user engagement. Even though it is observed that the selected posts on average have higher user engagement, some highly commented and liked posts might not be selected due to the effect of other features.
- Posts of certain linguistic styles can be filtered out as the new API tends to return posts with more emotional texts.
- Non-random factors might be influencing the representation of most prominent terms in the selected posts, which could lead to bias in text models.
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