When Computers Decide: European Recommendations on Machine-Learned Automated Decision Making

Larus, James R.; Hankin, Chris; Carson, Siri Granum; Christen, Markus; Crafa, Silvia; Grau, Oliver; Kirchner, Claude; Knowles, Bran; McGettrick, Andrew; Tamburri, Damian A.; Werthner, Hannes

doi:10.1145/3185595

Cited by 21 publications

(11 citation statements)

References 4 publications

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“…The increased interest in algorithmic decision-making in recent years has given rise to numerous calls for rules and guidelines for the ethical design and implementation of such systems. What unites many of these guidelines [e.g., Pinto, 2017 ; US Public Policy Council (USACM), 2017 ; Larus et al, 2018 ] is the argument that, in order to utilize them in a safe, fair and ethical manner, the accountability of these systems must be guaranteed. While transparency as a guiding principle is not the only factor—and certainly not a determinant one (Ananny and Crawford, 2016 )—the apparent opacity of many algorithmic systems represents a serious hindrance to holding them or their creators accountable (Pasquale, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

et al. 2020

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As of 2020, the Public Employment Service Austria (AMS) makes use of algorithmic profiling of job seekers to increase the efficiency of its counseling process and the effectiveness of active labor market programs. Based on a statistical model of job seekers' prospects on the labor market, the system—that has become known as the AMS algorithm—is designed to classify clients of the AMS into three categories: those with high chances to find a job within half a year, those with mediocre prospects on the job market, and those clients with a bad outlook of employment in the next 2 years. Depending on the category a particular job seeker is classified under, they will be offered differing support in (re)entering the labor market. Based in science and technology studies, critical data studies and research on fairness, accountability and transparency of algorithmic systems, this paper examines the inherent politics of the AMS algorithm. An in-depth analysis of relevant technical documentation and policy documents investigates crucial conceptual, technical, and social implications of the system. The analysis shows how the design of the algorithm is influenced by technical affordances, but also by social values, norms, and goals. A discussion of the tensions, challenges and possible biases that the system entails calls into question the objectivity and neutrality of data claims and of high hopes pinned on evidence-based decision-making. In this way, the paper sheds light on the coproduction of (semi)automated managerial practices in employment agencies and the framing of unemployment under austerity politics.

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Section: Discussionmentioning

confidence: 99%

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

et al. 2020

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“…The paper aims to contribute to a series of contemporary HCI concerns 4 . These include the potential applications of machine learning [50]; guidelines for implementing responsible, ethical and transparent AI systems [26,29]; and the emergence of Human-Data Interaction as a sub-discipline of HCI. Alongside the explosion of applications of AI (which, more often than not refers to variants of Machine Learning), recent years have seen a proliferation of frameworks, guidelines, and manifestos intended to support, encourage, or underpin ethical, transparent, and responsible system development [1,2,5,25].…”

Section: Discussionmentioning

confidence: 99%

Context-Aware Wearables

Snooks

Lindley

Richards

et al. 2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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We present Connected Companion (CoCo), a health tracking wearable that provides users with timely, context-relevant notifications aimed at improving wellness. Traditionally, self-tracking wearables report basic health data such as resting heart rate; these data are visualised and positive behaviours (e.g. exercising often) are encouraged with rudimentary gamification (e.g. award badges) and notification systems. CoCo is the first wearable to combine caffeine, alcohol and cortisol sensors, a context network (which predicts user context), and a wellness model (which establishes per-user wellness measures). Working in tandem these provide users with notifications that encourage discrete behaviours intended to optimise user-wellness per very specific biological and social contexts. The paper describes the (sometimes unexpected) results of a user-study intended to evaluate CoCo's efficacy and we conclude with a discussion about the power and responsibility that comes with attempts to build context-aware computing systems. CCS CONCEPTS • Human-centered computing à User interface design; Information visualization; • Applied computing à Health informatics; • Computer systems organization à Sensors and actuators; • General and reference à Design.

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“…Algorithmic influence is equally present in other types of social machines, such as ADM algorithms. ADM systems are largely used for risk assessment and warning (Mosier and Skitka 1996), for automating tasks such as image recognition, speech understanding, medical consulting, and predictive policing (Larus et al 2018;Ensign et al 2017;Dressel and Farid 2018). ADM systems result in bidirectional algorithmic influence.…”

Section: Algorithmic Influencementioning

confidence: 99%

Political machines: a framework for studying politics in social machines

Papakyriakopoulos

2021

AI & Soc

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In the age of ubiquitous computing and artificially intelligent applications, social machines serves as a powerful framework for understanding and interpreting interactions in socio-algorithmic ecosystems. Although researchers have largely used it to analyze the interactions of individuals and algorithms, limited attempts have been made to investigate the politics in social machines. In this study, I claim that social machines are per se political machines, and introduce a five-point framework for classifying influence processes in socio-algorithmic ecosystems. By drawing from scholars from political theory, I use a notion of influence that functions as a meta-concept for connecting and comparing different conceptions of politics. In this way, I can associate multiple political aspects of social machines from a cybernetic perspective. I show that the framework efficiently categorizes dimensions of influence that shape interactions between individuals and algorithms. These categories are symbolic influence, political conduct, algorithmic influence, design, and regulatory influence. Using case studies, I describe how they interact with each other on online social networks and in algorithmic decision-making systems and illustrate how the framework is able to guide scientists in further research.

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When Computers Decide: European Recommendations on Machine-Learned Automated Decision Making

Cited by 21 publications

References 4 publications

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective

Context-Aware Wearables

Political machines: a framework for studying politics in social machines

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