What to account for when accounting for algorithms

Abstract

As research on algorithms and their impact proliferates, so do calls for scrutiny/accountability of algorithms. A systematic review of the work that has been done in the field of 'algorithmic accountability' has so far been lacking. This contribution puts forth such a systematic review, following the PRISMA statement. 242 English articles from the period 2008 up to and including 2018 were collected and extracted from Web of Science and SCOPUS, using a recursive query design coupled with computational methods. The 242 articles were prioritized and ordered using affinity mapping, resulting in 93 'core articles' which are presented in this contribution. The recursive search strategy made it possible to look beyond the term 'algorithmic accountability'. That is, the query also included terms closely connected to the theme (e.g. ethics and AI, regulation of algorithms). This approach allows for a perspective not just from critical algorithm studies, but an interdisciplinary overview drawing on material from data studies to law, and from computer science to governance studies. To structure the material, Bovens's widely accepted definition of accountability serves as a focal point. The material is analyzed on the five points Bovens identified as integral to accountability: its arguments on (1) the actor, (2) the forum, (3) the relationship between the two, (3) the content and criteria of the account, and finally (5) the consequences which may result from the account. The review makes three contributions. First, an integration of accountability theory in the algorithmic accountability discussion. Second, a cross-sectoral overview of the that same discussion viewed in light of accountability theory which pays extra attention to accountability risks in algorithmic systems. Lastly, it provides a definition of algorithmic accountability based on accountability theory and algorithmic accountability literature. CCS CONCEPTS• Social and professional topics → Management of computing and information systems; Socio-technical systems; • General and reference; • Human-centered computing → Collaborative and social computing;

Towards Transparency by Design for Artificial Intelligence

“…Accountability refers to "a relationship between an actor and a forum, in which the actor has an obligation to explain and to justify his or her conduct, the forum can pose questions and pass judgment, and the actor may face the consequences" (Bovens 2007, p. 447). This definition highlights the various interacting elements of accountability: the actor, the forum, the relationship between the actor and forum, the content and criteria of the account, and finally, the consequences that can be imposed (Wieringa 2020), which connects it necessarily to a broader multidimensional understanding of the concept. The forum-or whom transparency is directed to-will necessarily shape the form and content of how to ensure accountability.…”

How Teams Communicate about the Quality of ML Models: A Case Study at an International Technology Company

“…As machine learning is becoming an integral part of our lives, researchers have been investigating the biases that could arise and how to mitigate them [5,44]. Work on bias mitigation looked into the interpretability and transparency of these models [27,59,60], what industry practitioners need to improve the fairness in ML systems [29,30], and the perceived fairness of biased algorithms in current practices [26,51]. In our work, we looked into how much bias and fairness in algorithms is communicated as an aspect of ML models' quality and who within teams and organizations is interested in this.…”

Generation of Synthetical Medical Data by MDR-Analysis

“…Synthetic data is of great value, for the reason that with a small amount of data it will be difficult to teach the same machine learning algorithms, or with an introduction of the new data there will be a considerable number of errors. For example, the project of ImageNet [2], aimed to solve the machine vision problem contains more than 14 million images, that are divided into 22 thousand categories. Due to the amount of material, the objects recognition algorithms are wrong only in 3.75% of cases, for comparison, a human being is wrong in more than 5% of cases [3].…”